CoRL
Collection
Accepted papers for CoRL (Conference on Robot Learning), one dataset per year. • 10 items • Updated
paper_id stringlengths 10 12 | title stringlengths 22 153 | paper_url stringlengths 42 44 | authors listlengths 2 54 | type stringclasses 2
values | primary_area stringclasses 0
values | abstract large_stringlengths 636 1.92k | keywords listlengths 0 7 | TL;DR large_stringlengths 62 250 ⌀ | submission_number int64 2 521 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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|---|---|---|---|---|---|---|---|---|---|---|---|
-K7-1WvKO3F | ViNT: A Foundation Model for Visual Navigation | https://openreview.net/forum?id=-K7-1WvKO3F | [
"Dhruv Shah",
"Ajay Sridhar",
"Nitish Dashora",
"Kyle Stachowicz",
"Kevin Black",
"Noriaki Hirose",
"Sergey Levine"
] | Oral | null | General-purpose pre-trained models (``foundation models'') have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets wi... | [
"visual navigation",
"multi-task learning",
"planning",
"generalization"
] | Large Transformer-based backbone for navigation, trained with cross-embodiment datasets, that supports broad generalization and efficient adaptation to downstream tasks. | 2 | 2306.14846 | title_snapshot |
PsV65r0itpo | Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning | https://openreview.net/forum?id=PsV65r0itpo | [
"Dhruv Shah",
"Michael Robert Equi",
"Błażej Osiński",
"Fei Xia",
"brian ichter",
"Sergey Levine"
] | Poster | null | Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration. Humans can rapid... | [
"navigation",
"language models",
"planning",
"semantics"
] | Using LLMs as heuristics for planning outperforms popular LLM planning methods and achieves great results on sim benchmarks and real-world evals! | 3 | 2310.10103 | title_snapshot |
XsWGVbPfB4Z | 4D-Former: Multimodal 4D Panoptic Segmentation | https://openreview.net/forum?id=XsWGVbPfB4Z | [
"Ali Athar",
"Enxu Li",
"Sergio Casas",
"Raquel Urtasun"
] | Poster | null | 4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time. Existing approaches utilize only LiDAR inputs which convey limited information in regions w... | [
"Panoptic Segmentation",
"Sensor Fusion",
"Temporal Reasoning",
"Autonomous Driving"
] | We propose 4D-Former: a novel method for 4D panoptic segmentation which leverages both LiDAR and image modalities, and predicts semantic masks as well as temporally consistent object masks for the input point-cloud sequence. | 4 | 2311.01520 | title_snapshot |
dgwvY3H8PAS | Dynamic Handover: Throw and Catch with Bimanual Hands | https://openreview.net/forum?id=dgwvY3H8PAS | [
"Binghao Huang",
"Yuanpei Chen",
"Tianyu Wang",
"Yuzhe Qin",
"Yaodong Yang",
"Nikolay Atanasov",
"Xiaolong Wang"
] | Poster | null | Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this paper, we design a system with two multi-finger hands... | [
"Bimanual Dexterous Manipulation",
"Sim-to-Real Transfer"
] | We propose a throwing and catching system using bimanual dexterous hands with reinforcement learning. | 9 | 2309.05655 | title_snapshot |
FRKBdXhkQE0 | FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing | https://openreview.net/forum?id=FRKBdXhkQE0 | [
"Kyle Stachowicz",
"Dhruv Shah",
"Arjun Bhorkar",
"Ilya Kostrikov",
"Sergey Levine"
] | Poster | null | We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. Our system integrat... | [
"reinforcement learning",
"offroad driving",
"vision-based navigation"
] | Training a task-specific vision encoder (using offline RL to learn representations) enables online learning from pixels (without simulation) in under 20 minutes. | 10 | 2304.09831 | title_snapshot |
9cTEQWMo1BF | LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds | https://openreview.net/forum?id=9cTEQWMo1BF | [
"Anqi Joyce Yang",
"Sergio Casas",
"Nikita Dvornik",
"Sean Segal",
"Yuwen Xiong",
"Jordan Sir Kwang Hu",
"Carter Fang",
"Raquel Urtasun"
] | Poster | null | A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage “auto-labelling” offboard perception models that are trained to automatically generate annotations from raw LiDAR point clouds at a fraction of the cost... | [
"Auto-labelling",
"Offboard Perception",
"Trajectory Refinement",
"Transformer"
] | We propose LabelFormer, a simple, efficient and effective transformer-based trajectory refinement method that reasons with full temporal context for LiDAR-based auto-labelling. | 12 | 2311.01444 | title_snapshot |
o82EXEK5hu6 | Parting with Misconceptions about Learning-based Vehicle Motion Planning | https://openreview.net/forum?id=o82EXEK5hu6 | [
"Daniel Dauner",
"Marcel Hallgarten",
"Andreas Geiger",
"Kashyap Chitta"
] | Poster | null | The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting. Existing systems struggle to simultaneously meet both requirements. Indeed, we find that these ... | [
"Motion Planning",
"Autonomous Driving",
"Data-driven Simulation"
] | null | 14 | 2306.07962 | title_snapshot |
UZpWSDA3tZJ | Towards General Single-Utensil Food Acquisition with Human-Informed Actions | https://openreview.net/forum?id=UZpWSDA3tZJ | [
"Ethan Kroll Gordon",
"Amal Nanavati",
"Ramya Challa",
"Bernie Hao Zhu",
"Taylor Annette Kessler Faulkner",
"Siddhartha Srinivasa"
] | Poster | null | Food acquisition with common general-purpose utensils is a necessary component of robot applications like in-home assistive feeding. Learning acquisition policies in this space is difficult in part because any model will need to contend with extensive state and actions spaces. Food is extremely diverse and generally di... | [
"Manipulation",
"Learning from Demonstration",
"Assistive Robotics"
] | Distilling a structured action space a priori from human demonstrations can lead to faster online learning as demonstrated with robotic food manipulation. | 15 | null | null |
0hPkttoGAf | RVT: Robotic View Transformer for 3D Object Manipulation | https://openreview.net/forum?id=0hPkttoGAf | [
"Ankit Goyal",
"Jie Xu",
"Yijie Guo",
"Valts Blukis",
"Yu-Wei Chao",
"Dieter Fox"
] | Oral | null | For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulati... | [
"3D Manipulation",
"Multi-View",
"Transformer"
] | null | 18 | 2306.14896 | title_snapshot |
ES_TOp4YJeD | ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation | https://openreview.net/forum?id=ES_TOp4YJeD | [
"ZiZhang Wu",
"Zhuozheng Li",
"Zhi-Gang Fan",
"Yunzhe Wu",
"Xiaoquan Wang",
"Rui Tang",
"Jian Pu"
] | Poster | null | Monocular depth estimation is challenging due to its inherent ambiguity and ill-posed nature, yet it is quite important to many applications. While recent works achieve limited accuracy by designing increasingly complicated networks to extract features with limited spatial geometric cues from a single RGB image, we int... | [
"Monocular depth estimation",
"Camera perception",
"Distillation"
] | The top performer for monocular depth estimation to date is based on the proposed novel distillation scheme and ranked 1st on the challenging KITTI online benchmark. | 26 | 2309.14744 | title_snapshot |
ihqTtzS83VS | Learning Reusable Manipulation Strategies | https://openreview.net/forum?id=ihqTtzS83VS | [
"Jiayuan Mao",
"Tomás Lozano-Pérez",
"Joshua B. Tenenbaum",
"Leslie Pack Kaelbling"
] | Poster | null | Humans demonstrate an impressive ability to acquire and generalize manipulation "tricks." Even from a single demonstration, such as using soup ladles to reach for distant objects, we can apply this skill to new scenarios involving different object positions, sizes, and categories (e.g., forks and hammers). Additionally... | [
"Contact Modeling and Manipulation",
"Task and Motion Planning"
] | null | 35 | 2311.03293 | title_snapshot |
IeKC9khX5jD | Affordance-Driven Next-Best-View Planning for Robotic Grasping | https://openreview.net/forum?id=IeKC9khX5jD | [
"Xuechao Zhang",
"Dong Wang",
"Sun Han",
"Weichuang Li",
"Bin Zhao",
"Zhigang Wang",
"Xiaoming Duan",
"Chongrong Fang",
"Xuelong Li",
"Jianping He"
] | Poster | null | Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a feasible grasp for target object via continuously observing scenes from new viewpoints. This... | [
"Grasp Synthesis",
"Neural SDF",
"Next-Best-View Planning"
] | null | 43 | 2309.09556 | title_snapshot |
psLlVbTFBua | FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection | https://openreview.net/forum?id=psLlVbTFBua | [
"Harry Zhang",
"Ben Eisner",
"David Held"
] | Poster | null | Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interaction, after training on other articulated objects. Previous approaches for articulated object... | [
"Articulated objects manipulation",
"representation learning"
] | A novel learned visual representation for articulated objects manipulation | 45 | 2306.12893 | title_snapshot |
Q9ezhChqnL | Towards Scalable Coverage-Based Testing of Autonomous Vehicles | https://openreview.net/forum?id=Q9ezhChqnL | [
"James Tu",
"Simon Suo",
"Chris Zhang",
"Kelvin Wong",
"Raquel Urtasun"
] | Poster | null | To deploy autonomous vehicles(AVs) in the real world, developers must understand the conditions in which the system can operate safely. To do this in a scalable manner, AVs are often tested in simulation on parameterized scenarios. In this context, it’s important to build a testing framework that partitions the scenari... | [
"Self-Driving",
"Coverage",
"Testing"
] | We propose a scalable testing framework for testing self-driving vehicles in parameterized scenario. | 46 | null | null |
eyykI3UIHa | NOIR: Neural Signal Operated Intelligent Robots for Everyday Activities | https://openreview.net/forum?id=eyykI3UIHa | [
"Ruohan Zhang",
"Sharon Lee",
"Minjune Hwang",
"Ayano Hiranaka",
"Chen Wang",
"Wensi Ai",
"Jin Jie Ryan Tan",
"Shreya Gupta",
"Yilun Hao",
"Gabrael Levine",
"Ruohan Gao",
"Anthony Norcia",
"Li Fei-Fei",
"Jiajun Wu"
] | Poster | null | We present Neural Signal Operated Intelligent Robots (NOIR), a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. Through this interface, humans communicate their intended objects of interest and actions to the robots usi... | [
"Brain-Robot Interface",
"Human-Robot Interaction"
] | We present a general-purpose, intelligent brain-robot interface system that enables humans to command robots to perform everyday activities through brain signals. | 47 | 2311.01454 | title_snapshot |
8yTS_nAILxt | REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction | https://openreview.net/forum?id=8yTS_nAILxt | [
"Zeyi Liu",
"Arpit Bahety",
"Shuran Song"
] | Poster | null | The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong reasoning abilities on textual inputs. To leverage the power of LLMs for robot failure explanation, we introduce REFLECT, a framework... | [
"Large Language Model",
"Explainable AI",
"Task Planning"
] | We propose REFLECT, a framework that leverages Large Language Models (LLMs) for robot failure explanation and correction, based on a hierarchical summary of robot past experiences generated from multisensory data. | 48 | 2306.15724 | title_snapshot |
EXQ0eXtX3OW | Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play | https://openreview.net/forum?id=EXQ0eXtX3OW | [
"Irmak Guzey",
"Ben Evans",
"Soumith Chintala",
"Lerrel Pinto"
] | Poster | null | Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either operate on visual observations or state estimates derived from vision. However, such methods perform poorly on fine-grained manipulation tas... | [
"Tactile",
"Dexterity",
"Manipulation"
] | Tactile pretraining from play data for dexterous manipulation. | 49 | 2303.12076 | title_snapshot |
b1tl3aOt2R2 | GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields | https://openreview.net/forum?id=b1tl3aOt2R2 | [
"Yanjie Ze",
"Ge Yan",
"Yueh-Hua Wu",
"Annabella Macaluso",
"Yuying Ge",
"Jianglong Ye",
"Nicklas Hansen",
"Li Erran Li",
"Xiaolong Wang"
] | Oral | null | It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot will need to have a comprehensive understanding of the 3D structure and semantics of the scene. In this work, we ... | [
"Robotic Manipulation",
"Neural Radiance Field",
"Behavior Cloning"
] | null | 50 | 2308.16891 | title_snapshot |
8L6pHd9aS6w | XSkill: Cross Embodiment Skill Discovery | https://openreview.net/forum?id=8L6pHd9aS6w | [
"Mengda Xu",
"Zhenjia Xu",
"Cheng Chi",
"Manuela Veloso",
"Shuran Song"
] | Poster | null | Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior. However, directly extracting reusable robot manipulation skills from unstructured human videos is challenging due to the big embodiment difference and unobserved action para... | [
"Manipulation",
"Representation Learning",
"Cross-Embodiements"
] | We present XSkill, an imitation learning framework that can discover, transfer and compose skills from cross-embodiment demonstration. | 51 | 2307.09955 | title_snapshot |
nyY6UgXYyfF | Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation | https://openreview.net/forum?id=nyY6UgXYyfF | [
"Jay Sarva",
"Jingkang Wang",
"James Tu",
"Yuwen Xiong",
"Sivabalan Manivasagam",
"Raquel Urtasun"
] | Poster | null | Self-driving vehicles (SDVs) must be rigorously tested on a wide range of scenarios to ensure safe deployment. The industry typically relies on closed-loop simulation to evaluate how the SDV interacts on a corpus of synthetic and real scenarios and to verify good performance. However, they primarily only test the motio... | [
"closed-loop simulation",
"adversarial robustness",
"self-driving"
] | We propose a system to generate safety-critical actor shapes for autonomy testing through closed-loop simulation of real-world traffic scenarios | 56 | 2311.01446 | title_snapshot |
JkFeyEC6VXV | Finetuning Offline World Models in the Real World | https://openreview.net/forum?id=JkFeyEC6VXV | [
"Yunhai Feng",
"Nicklas Hansen",
"Ziyan Xiong",
"Chandramouli Rajagopalan",
"Xiaolong Wang"
] | Oral | null | Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to learn skills. Recently, offline RL has been proposed as a framework for t... | [
"Model-Based Reinforcement Learning",
"Real-World Robotics"
] | We propose a framework for offline-to-online finetuning of world models and demonstrate its effectiveness in real robot tasks. | 57 | 2310.16029 | title_snapshot |
yobahDU4HPP | Learning Realistic Traffic Agents in Closed-loop | https://openreview.net/forum?id=yobahDU4HPP | [
"Chris Zhang",
"James Tu",
"Lunjun Zhang",
"Kelvin Wong",
"Simon Suo",
"Raquel Urtasun"
] | Poster | null | Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment. Typically, imitation learning (IL) is used to learn human-like traffic agents directly from real-world observations collected offline, but without explicit specification of traffic ... | [
"Traffic simulation",
"Imitation learning",
"Reinforcement learning"
] | We learn realistic traffic simulation agents in closed-loop using a joint IL + RL approach that exploits nominal offline-collected data and simulated long-tail scenarios. | 63 | 2311.01394 | title_snapshot |
44FPaVRWkbl | DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking | https://openreview.net/forum?id=44FPaVRWkbl | [
"Qing LIAN",
"Tai Wang",
"Dahua Lin",
"Jiangmiao Pang"
] | Poster | null | Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis... | [
"Temporal Modeling",
"3D Object Detection"
] | null | 65 | 2303.16628 | title_snapshot |
7TYeO2XVqI | SayTap: Language to Quadrupedal Locomotion | https://openreview.net/forum?id=7TYeO2XVqI | [
"Yujin Tang",
"Wenhao Yu",
"Jie Tan",
"Heiga Zen",
"Aleksandra Faust",
"Tatsuya Harada"
] | Poster | null | Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or motor torques. This paper proposes an approach to use foot contact patterns as an interface that bridges human commands in na... | [
"Large language model (LLM)",
"Quadrupedal robots",
"Locomotion"
] | We propose to use foot contact pattern as an interface to bridge human commands in natural language and low-level commands | 68 | 2306.07580 | title_snapshot |
UVARkqnsDd | ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction | https://openreview.net/forum?id=UVARkqnsDd | [
"Jingyi Yu",
"Zizhao Zhang",
"Shengfu Xia",
"Jizhang Sang"
] | Poster | null | We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with r... | [
"Map Construction",
"Multi-view Perception",
"Long-range Perception"
] | We exploit the structural properties of vectorized map elements to address the challenges of accurately detecting map elements at longer ranges. | 72 | 2310.13378 | title_snapshot |
fa7FzDjhzs9 | HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation | https://openreview.net/forum?id=fa7FzDjhzs9 | [
"Wenxuan Zhou",
"Bowen Jiang",
"Fan Yang",
"Chris Paxton",
"David Held"
] | Oral | null | Manipulating objects without grasping them is an essential component of human dexterity, referred to as non-prehensile manipulation. Non-prehensile manipulation may enable more complex interactions with the objects, but also presents challenges in reasoning about gripper-object interactions. In this work, we introduce ... | [
"Action Representation",
"Reinforcement Learning with 3D Vision",
"Non-prehensile Manipulation"
] | We propose a reinforcement learning framework with an object-centric action representation defined over point cloud observations and evaluate it with a 6D non-prehensile manipulation task. | 77 | 2305.03942 | title_snapshot |
pLCQkMojXI | Rearrangement Planning for General Part Assembly | https://openreview.net/forum?id=pLCQkMojXI | [
"Yulong Li",
"Andy Zeng",
"Shuran Song"
] | Oral | null | Most successes in autonomous robotic assembly have been restricted to single target or category. We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes. As a fundamental step to a general part assembly system, we tackle the task of determining the precise p... | [
"robotic assembly",
"pose estimation",
"3D perception"
] | We propose to investigate general part assembly, the task of creating novel target assemblies with unseen part shapes, and introduce General Part Assembly Transformer (GPAT) for the task. | 81 | 2307.00206 | title_snapshot |
fSmkKmWM5Ry | Stochastic Occupancy Grid Map Prediction in Dynamic Scenes | https://openreview.net/forum?id=fSmkKmWM5Ry | [
"Zhanteng Xie",
"Philip Dames"
] | Poster | null | This paper presents two variations of a novel stochastic prediction algorithm that enables mobile robots to accurately and robustly predict the future state of complex dynamic scenes. The proposed algorithm uses a variational autoencoder to predict a range of possible future states of the environment. The algorithm tak... | [
"Environment Prediction",
"Probabilistic Inference",
"Robot Learning"
] | We use techniques from generative AI to create a probabilistic prediction of the future to improve mobile robot navigation in dynamic spaces | 82 | 2210.08577 | title_snapshot |
QNPuJZyhFE | Imitating Task and Motion Planning with Visuomotor Transformers | https://openreview.net/forum?id=QNPuJZyhFE | [
"Murtaza Dalal",
"Ajay Mandlekar",
"Caelan Reed Garrett",
"Ankur Handa",
"Ruslan Salakhutdinov",
"Dieter Fox"
] | Poster | null | Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contras... | [
"Imitation Learning",
"Task and Motion Planning",
"Transformers"
] | We propose a novel method for training large scale imitation policies for robotic manipulation by distilling Task and Motion Planning into visuomotor Transformers. | 83 | 2305.16309 | title_snapshot |
RQ_7yVV8vA | Learning to See Physical Properties with Active Sensing Motor Policies | https://openreview.net/forum?id=RQ_7yVV8vA | [
"Gabriel B. Margolis",
"Xiang Fu",
"Yandong Ji",
"Pulkit Agrawal"
] | Poster | null | To plan efficient robot locomotion, we must use the information about a terrain’s physics that can be inferred from color images. To this end, we train a visual perception module that predicts terrain properties using labels from a small amount of real-world proprioceptive locomotion. To ensure label precision, we intr... | [
"Locomotion",
"Vision",
"Navigation",
"Reinforcement Learning"
] | A legged robot learns to measure terrain properties more accurately by intentionally feeling the ground; its feelings serve as self-supervised labels for a visual perception module; this facilitates locomotion planning in diverse configurations. | 89 | 2311.01405 | title_snapshot |
sLhk0keeiseH | That Sounds Right: Auditory Self-Supervision for Dynamic Robot Manipulation | https://openreview.net/forum?id=sLhk0keeiseH | [
"Abitha Thankaraj",
"Lerrel Pinto"
] | Poster | null | Learning to produce contact-rich, dynamic behaviors from raw sensory data has been a longstanding challenge in robotics. Prominent approaches primarily focus on using visual and tactile sensing. However, pure vision often fails to capture high-frequency interaction, while current tactile sensors can be too delicate for... | [
"Dynamic manipulation",
"Self supervised learning",
"Audio"
] | Learning contact-rich, dynamic manipulation behaviors using self-supervised techniques in audio. | 90 | 2210.01116 | title_snapshot |
69y5fzvaAT | RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools | https://openreview.net/forum?id=69y5fzvaAT | [
"Haochen Shi",
"Huazhe Xu",
"Samuel Clarke",
"Yunzhu Li",
"Jiajun Wu"
] | Oral | null | Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots remains limited due to challenges in understanding tool-object interac... | [
"Deformable Object Manipulation",
"Long-horizon Planning",
"Model Learning",
"Tool Usage"
] | RoboCook perceives, models, and manipulates elasto-plastic objects for long-horizon tasks like making dumplings and alphabet letter cookies. | 92 | 2306.14447 | title_snapshot |
VtJqMs9ig20 | CAT: Closed-loop Adversarial Training for Safe End-to-End Driving | https://openreview.net/forum?id=VtJqMs9ig20 | [
"Linrui Zhang",
"Zhenghao Peng",
"Quanyi Li",
"Bolei Zhou"
] | Poster | null | Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling accident-prone traffic events by algorithm designs at the policy level, we investigate a \textbf{C}losed-loop \textbf{A}dversarial \textbf{T}raining (CAT) framework for safe end-to-end driving in this paper through the lens of e... | [
"Safety-Critical Scenario Generation",
"Adversarial Training",
"End-to-End Driving"
] | null | 93 | 2310.12432 | title_snapshot |
5JMGq83yf1N | Ready, Set, Plan! Planning to Goal Sets Using Generalized Bayesian Inference | https://openreview.net/forum?id=5JMGq83yf1N | [
"Jana Pavlasek",
"Stanley Robert Lewis",
"Balakumar Sundaralingam",
"Fabio Ramos",
"Tucker Hermans"
] | Poster | null | Many robotic tasks can have multiple and diverse solutions and, as such, are naturally expressed as goal sets. Examples include navigating to a room, finding a feasible placement location for an object, or opening a drawer enough to reach inside. Using a goal set as a planning objective requires that a model for the ob... | [
"Planning as inference",
"Variational inference",
"Nonparametric learning"
] | We propose a goal set planner which plans to uncertain goal regions using demonstrations of valid goals. | 96 | null | null |
HCWoFkGe8L4 | Revisiting Depth-guided Methods for Monocular 3D Object Detection by Hierarchical Balanced Depth | https://openreview.net/forum?id=HCWoFkGe8L4 | [
"Yi-Rong Chen",
"Ching-Yu Tseng",
"Yi-Syuan Liou",
"Tsung-Han Wu",
"Winston H. Hsu"
] | Poster | null | Monocular 3D object detection has seen significant advancements with the incorporation of depth information. However, there remains a considerable performance gap compared to LiDAR-based methods, largely due to inaccurate depth estimation. We argue that this issue stems from the commonly used pixel-wise depth map loss,... | [
"monocular 3D object detection",
"autonomous driving"
] | null | 97 | null | null |
W5SrUCN0yUa | A Bayesian Approach to Robust Inverse Reinforcement Learning | https://openreview.net/forum?id=W5SrUCN0yUa | [
"Ran Wei",
"Siliang Zeng",
"Chenliang Li",
"Alfredo Garcia",
"Anthony D McDonald",
"Mingyi Hong"
] | Poster | null | We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL). The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. We make use of a class of prior... | [
"Inverse Reinforcement Learning",
"Bayesian Inference",
"Robustness"
] | null | 101 | 2309.08571 | title_snapshot |
3uwj8QZROL | Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition | https://openreview.net/forum?id=3uwj8QZROL | [
"Huy Ha",
"Pete Florence",
"Shuran Song"
] | Poster | null | We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sa... | [
"skill learning",
"language",
"diffusion"
] | We present a framework for language-model guided data generation over robot utilities, and multi-task language-conditioned diffusion policy distillation. | 104 | 2307.14535 | title_snapshot |
w5ONmpgnfG | One-Shot Imitation Learning: A Pose Estimation Perspective | https://openreview.net/forum?id=w5ONmpgnfG | [
"Pietro Vitiello",
"Kamil Dreczkowski",
"Edward Johns"
] | Poster | null | In this paper, we study imitation learning under the challenging setting of: (1) only a single demonstration, (2) no further data collection, and (3) no prior task or object knowledge. We show how, with these constraints, imitation learning can be formulated as a combination of trajectory transfer and unseen object pos... | [
"One-Shot Imitation Learning",
"Unseen Object Pose Estimation",
"Robot Manipulation"
] | We study one-shot imitation learning from the perspective of unseen object pose estimation, providing valuable insights as well as showing this framework's potential in real world robotics tasks. | 106 | 2310.12077 | title_snapshot |
rPye6EZxmI | Reinforcement Learning Enables Real-Time Planning and Control of Agile Maneuvers for Soft Robot Arms | https://openreview.net/forum?id=rPye6EZxmI | [
"Rianna Jitosho",
"Tyler Ga Wei Lum",
"Allison Okamura",
"Karen Liu"
] | Poster | null | Control policies for soft robot arms typically assume quasi-static motion or require a hand-designed motion plan. To achieve real-time planning and control for tasks requiring highly dynamic maneuvers, we apply deep reinforcement learning to train a policy entirely in simulation, and we identify strategies and insights... | [
"Soft Robotics",
"Reinforcement Learning",
"Sim-to-Real Transfer",
"Dynamics and Control"
] | This is the first work that demonstrates real-time planning and control of agile maneuvers by soft robot arms, which is achieved by using reinforcement learning and key insights to overcome sim-to-real challenges for zero-shot sim-to-real transfer. | 108 | null | null |
eE3fsO5Mi2 | Stealthy Terrain-Aware Multi-Agent Active Search | https://openreview.net/forum?id=eE3fsO5Mi2 | [
"Nikhil Angad Bakshi",
"Jeff Schneider"
] | Poster | null | Stealthy multi-agent active search is the problem of making efficient sequential data-collection decisions to identify an unknown number of sparsely located targets while adapting to new sensing information and concealing the search agents' location from the targets. This problem is applicable to reconnaissance tasks w... | [
"Reconnaissance",
"Adversarial Search",
"Multi-robot",
"Active Learning"
] | Leveraging a known terrain map and Thompson sampling based active learning to set new state of the art in search efficiency with minimal visibility risk in realistic search settings. | 109 | 2310.10961 | title_snapshot |
48qUHKUEdBf | STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots | https://openreview.net/forum?id=48qUHKUEdBf | [
"Yi Li",
"Muru Zhang",
"Markus Grotz",
"Kaichun Mo",
"Dieter Fox"
] | Poster | null | Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here, robots must handle object rearrangements, including shifting, removal, and partial occlusion by new items, and track these items after subst... | [
"Unseen Object Instance Segmentation",
"Unsupervised Multi Object Tracking",
"Zero-shot",
"Discrete Frames"
] | We formulated the the task of unseen object segmentation and tracking in discrete frame which is essential for warehouse picking robots and proposed a novel method STOW which show significant improvements compared with its counterparts on this task. | 110 | 2311.02337 | title_snapshot |
JdpleC92J4 | AR2-D2: Training a Robot Without a Robot | https://openreview.net/forum?id=JdpleC92J4 | [
"Jiafei Duan",
"Yi Ru Wang",
"Mohit Shridhar",
"Dieter Fox",
"Ranjay Krishna"
] | Poster | null | Diligently gathered human demonstrations serve as the unsung heroes empowering the progression of robot learning.
Today, demonstrations are collected by training people to use specialized controllers, which (tele-)operate robots to manipulate a small number of objects. By contrast, we introduce AR2-D2: a system for col... | [
"Demonstration collection",
"Imitation learning",
"Augmented reality",
"Manipulating personalized objects",
"Dataset collection",
"Behavior Cloning"
] | A novel framework for collecting trainable robot demonstrations without the need for a real robot. | 111 | 2306.13818 | title_snapshot |
0I3su3mkuL | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions | https://openreview.net/forum?id=0I3su3mkuL | [
"Yevgen Chebotar",
"Quan Vuong",
"Karol Hausman",
"Fei Xia",
"Yao Lu",
"Alex Irpan",
"Aviral Kumar",
"Tianhe Yu",
"Alexander Herzog",
"Karl Pertsch",
"Keerthana Gopalakrishnan",
"Julian Ibarz",
"Ofir Nachum",
"Sumedh Anand Sontakke",
"Grecia Salazar",
"Huong T Tran",
"Jodilyn Peralta... | Poster | null | In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal diff... | [
"Reinforcement Learning",
"Offline RL",
"Transformers",
"Q-Learning",
"Robotic Manipulation"
] | null | 115 | 2309.10150 | title_snapshot |
6a4sECAMCA | Learning to Drive Anywhere | https://openreview.net/forum?id=6a4sECAMCA | [
"Ruizhao Zhu",
"Peng Huang",
"Eshed Ohn-Bar",
"Venkatesh Saligrama"
] | Poster | null | Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, \ie, without accounting f... | [
"Global-scale Autonomous Driving",
"Imitation Learning",
"Transformer"
] | null | 117 | 2309.12295 | title_snapshot |
SgTPdyehXMA | Language to Rewards for Robotic Skill Synthesis | https://openreview.net/forum?id=SgTPdyehXMA | [
"Wenhao Yu",
"Nimrod Gileadi",
"Chuyuan Fu",
"Sean Kirmani",
"Kuang-Huei Lee",
"Montserrat Gonzalez Arenas",
"Hao-Tien Lewis Chiang",
"Tom Erez",
"Leonard Hasenclever",
"Jan Humplik",
"brian ichter",
"Ted Xiao",
"Peng Xu",
"Andy Zeng",
"Tingnan Zhang",
"Nicolas Heess",
"Dorsa Sadigh"... | Oral | null | Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions ar... | [
"Large language model (LLM)",
"Low-level skill learning",
"Legged locomotion",
"Dexterous manipulation"
] | We propose to use reward function to bridge language model and low-level robot actions for interactive creation of novel behavior from human instructions. | 120 | 2306.08647 | title_snapshot |
gdkKi_F55h | SA6D: Self-Adaptive Few-Shot 6D Pose Estimator for Novel and Occluded Objects | https://openreview.net/forum?id=gdkKi_F55h | [
"Ning Gao",
"Vien Anh Ngo",
"Hanna Ziesche",
"Gerhard Neumann"
] | Poster | null | To enable meaningful robotic manipulation of objects in the real-world, 6D pose estimation is one of the critical aspects. Most existing approaches have difficulties to extend predictions to scenarios where novel object instances are continuously introduced, especially with heavy occlusions. In this work, we propose a ... | [] | null | 126 | 2308.16528 | title_snapshot |
oqOfLP6bJy | Contrastive Value Learning: Implicit Models for Simple Offline RL | https://openreview.net/forum?id=oqOfLP6bJy | [
"Bogdan Mazoure",
"Benjamin Eysenbach",
"Ofir Nachum",
"Jonathan Tompson"
] | Poster | null | Model-based reinforcement learning (RL) methods are appealing in the offline setting because they allow an agent to reason about the consequences of actions without interacting with the environment. While conventional model-based methods learn a 1-step model, predicting the immediate next state, these methods must be p... | [
"reinforcement learning",
"robotics",
"metaworld",
"unsupervised learning",
"contrastive learning",
"noise-contrastive estimation",
"generative model"
] | null | 133 | 2211.02100 | title_snapshot |
G_FEL3OkiR | Human-in-the-Loop Task and Motion Planning for Imitation Learning | https://openreview.net/forum?id=G_FEL3OkiR | [
"Ajay Mandlekar",
"Caelan Reed Garrett",
"Danfei Xu",
"Dieter Fox"
] | Poster | null | Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks, but they are difficult to apply to contact-rich tasks. In this paper, we prese... | [
"Imitation Learning",
"Task and Motion Planning",
"Teleoperation"
] | We present Human-in-the-Loop Task and Motion Planning (HITL-TAMP), a novel system that selectively gives and takes control to and from a human teleoperator, enabling more efficient imitation learning. | 135 | 2310.16014 | title_snapshot |
32c8pl84_uD | Marginalized Importance Sampling for Off-Environment Policy Evaluation | https://openreview.net/forum?id=32c8pl84_uD | [
"Pulkit Katdare",
"Nan Jiang",
"Katherine Rose Driggs-Campbell"
] | Poster | null | Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in simulation requires a real-world deployment to assess their performance. This paper proposes a new approach to evaluate the real-world perform... | [
"Sim2Real",
"Policy Evaluation",
"Robot Validation"
] | We propose a method of validating real-world performance of a robot using just the robot simulator and limited offline data | 136 | 2309.01807 | title_snapshot |
i84V7i6KEMd | Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards | https://openreview.net/forum?id=i84V7i6KEMd | [
"Katherine Metcalf",
"Miguel Sarabia",
"Natalie Mackraz",
"Barry-John Theobald"
] | Poster | null | Preference-based reinforcement learning (PbRL) aligns a robot behavior with human preferences via a reward function learned from binary feedback over agent behaviors. We show that encoding environment dynamics in the reward function improves the sample efficiency of PbRL by an order of magnitude. In our experiments we ... | [
"human-in-the-loop learning",
"preference-based RL",
"RLHF"
] | We provide state-action transition dynamics into a reward function learnt from trajectory preferences and find that we can obtain baseline performance with one order of magnitude fewer preferences. | 138 | 2402.17975 | title_snapshot |
CnKf9TyYtf2 | Few-Shot In-Context Imitation Learning via Implicit Graph Alignment | https://openreview.net/forum?id=CnKf9TyYtf2 | [
"Vitalis Vosylius",
"Edward Johns"
] | Poster | null | Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship betwe... | [
"Few-Shot Imitation Learning",
"Graph Neural Networks"
] | We learn how to align graph representations of objects and use it as a foundation of a few-shot in-context imitation learning framework. | 140 | 2310.12238 | title_snapshot |
XMQgwiJ7KSX | RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | https://openreview.net/forum?id=XMQgwiJ7KSX | [
"Brianna Zitkovich",
"Tianhe Yu",
"Sichun Xu",
"Peng Xu",
"Ted Xiao",
"Fei Xia",
"Jialin Wu",
"Paul Wohlhart",
"Stefan Welker",
"Ayzaan Wahid",
"Quan Vuong",
"Vincent Vanhoucke",
"Huong Tran",
"Radu Soricut",
"Anikait Singh",
"Jaspiar Singh",
"Pierre Sermanet",
"Pannag R Sanketi",
... | Poster | null | We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefit... | [
"vision-language models",
"robot manipulation",
"generalization"
] | Vision-language models, trained on Internet-scale data, can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. | 144 | 2307.15818 | title_snapshot |
B7PnAw4ze0l | Precise Robotic Needle-Threading with Tactile Perception and Reinforcement Learning | https://openreview.net/forum?id=B7PnAw4ze0l | [
"Zhenjun Yu",
"Wenqiang Xu",
"Siqiong Yao",
"Jieji Ren",
"Tutian Tang",
"Yutong Li",
"Guoying Gu",
"Cewu Lu"
] | Poster | null | This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: \textit{Tail-end Finding} and \textit{Tail-end Insertion}. In the first stage, the agent traces the ... | [
"tactile perception",
"needle threading"
] | A tactile-perception-based solution to robotic needle threading task | 146 | 2311.02396 | title_snapshot |
b-cto-fetlz | HomeRobot: Open-Vocabulary Mobile Manipulation | https://openreview.net/forum?id=b-cto-fetlz | [
"Sriram Yenamandra",
"Arun Ramachandran",
"Karmesh Yadav",
"Austin S Wang",
"Mukul Khanna",
"Theophile Gervet",
"Tsung-Yen Yang",
"Vidhi Jain",
"Alexander Clegg",
"John M Turner",
"Zsolt Kira",
"Manolis Savva",
"Angel X Chang",
"Devendra Singh Chaplot",
"Dhruv Batra",
"Roozbeh Mottaghi... | Poster | null | HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks.
Open-Vocabulary Mobile Manipulation (OVMM) is the problem of picking any object in any unseen environment, and placing it in a commanded location. This is a foundational cha... | [
"benchmark",
"mobile manipulation",
"sim2real"
] | We propose "open vocabulary mobile manipulation" as a key problem for robotics, and provide both a simulation and a reproducible real-world benchmark. | 148 | 2306.11565 | title_snapshot |
ANJuNDFdvP | UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding | https://openreview.net/forum?id=ANJuNDFdvP | [
"Han Xue",
"Yutong Li",
"Wenqiang Xu",
"Huanyu Li",
"Dongzhe Zheng",
"Cewu Lu"
] | Poster | null | This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfolding and folding decisions into a single policy model that is adaptable to different garm... | [
"Deformable Object Manipulation",
"Bimanual Manipulation",
"Garment Folding"
] | A unified solution for generalizable robotic garment unfolding and folding | 151 | 2311.01267 | title_snapshot |
dk-2R1f_LR | MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations | https://openreview.net/forum?id=dk-2R1f_LR | [
"Ajay Mandlekar",
"Soroush Nasiriany",
"Bowen Wen",
"Iretiayo Akinola",
"Yashraj Narang",
"Linxi Fan",
"Yuke Zhu",
"Dieter Fox"
] | Poster | null | Imitation learning from a large set of human demonstrations has proved to be an effective paradigm for building capable robot agents. However, the demonstrations can be extremely costly and time-consuming to collect. We introduce MimicGen, a system for automatically synthesizing large-scale, rich datasets from only a s... | [
"Imitation Learning",
"Manipulation"
] | We introduce MimicGen, a system for automatically synthesizing large-scale datasets from a small number of human demonstrations by adapting them to new scene configurations, object instances, and robot arms. | 154 | 2310.17596 | title_snapshot |
WGSR7HDuHu | Learning Robot Manipulation from Cross-Morphology Demonstration | https://openreview.net/forum?id=WGSR7HDuHu | [
"Gautam Salhotra",
"I-Chun Arthur Liu",
"Gaurav S. Sukhatme"
] | Poster | null | Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the casewhere the teacher’s morphology is substantially different from that of the student. Our framework, Morphological Adaptation in Imitation Learning (MAIL), bridges this gap allo... | [
"Imitation from Observation",
"Learning from Demonstration"
] | Generalizing LfD for manipulation to large mismatches between teacher and student morphologies | 159 | 2304.03833 | title_snapshot |
7Pkzm2FgUmq | SLAP: Spatial-Language Attention Policies | https://openreview.net/forum?id=7Pkzm2FgUmq | [
"Priyam Parashar",
"Vidhi Jain",
"Xiaohan Zhang",
"Jay Vakil",
"Sam Powers",
"Yonatan Bisk",
"Chris Paxton"
] | Poster | null | Despite great strides in language-guided manipulation, existing work has been constrained to table-top settings. Table-tops allow for perfect and consistent camera angles, properties are that do not hold in mobile manipulation. Task plans that involve moving around the environment must be robust to egocentric views and... | [
"learning from demonstration",
"language-based robotics"
] | We propose SLAP, a way of training vision+language policies which work for mobile robots and trains with very little data. | 160 | null | null |
zvl2LuLTtgr | What Went Wrong? Closing the Sim-to-Real Gap via Differentiable Causal Discovery | https://openreview.net/forum?id=zvl2LuLTtgr | [
"Peide Huang",
"Xilun Zhang",
"Ziang Cao",
"Shiqi Liu",
"Mengdi Xu",
"Wenhao Ding",
"Jonathan Francis",
"Bingqing Chen",
"Ding Zhao"
] | Poster | null | Training control policies in simulation is more appealing than on real robots directly, as it allows for exploring diverse states in an efficient manner. Yet, robot simulators inevitably exhibit disparities from the real-world \rebut{dynamics}, yielding inaccuracies that manifest as the dynamical simulation-to-reality ... | [
"sim-to-real gap",
"reinforcement learning",
"causal discovery"
] | We introduce an approach that aims to align the simulator with the real world by discovering the causality between the environment parameters and the sim-to-real gap. | 164 | 2306.15864 | title_snapshot |
_xFJuqBId8c | Shelving, Stacking, Hanging: Relational Pose Diffusion for Multi-modal Rearrangement | https://openreview.net/forum?id=_xFJuqBId8c | [
"Anthony Simeonov",
"Ankit Goyal",
"Lucas Manuelli",
"Yen-Chen Lin",
"Alina Sarmiento",
"Alberto Rodriguez Garcia",
"Pulkit Agrawal",
"Dieter Fox"
] | Poster | null | We propose a system for rearranging objects in a scene to achieve a desired object-scene placing relationship, such as a book inserted in an open slot of a bookshelf. The pipeline generalizes to novel geometries, poses, and layouts of both scenes and objects, and is trained from demonstrations to operate directly on 3D... | [
"Object Rearrangement",
"Multi-modality",
"Manipulation",
"Point Clouds",
"Relations",
"Diffusion"
] | A method for rearranging objects in scenes that present multi-modal placement locations via iterative pose de-noising on object-scene point clouds. | 167 | 2307.04751 | title_snapshot |
6kSohKYYTn0 | Measuring Interpretability of Neural Policies of Robots with Disentangled Representation | https://openreview.net/forum?id=6kSohKYYTn0 | [
"Tsun-Hsuan Wang",
"Wei Xiao",
"Tim Seyde",
"Ramin Hasani",
"Daniela Rus"
] | Oral | null | The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are mostly safety-critical systems. This urges a formal and quantitative u... | [
"Interpretability",
"Disentangled Representation",
"Neural Policy"
] | Investigate interpretability of compact neural policies with disentanglement and decision tree | 171 | null | null |
nKWQnYkkwX | Language-Guided Traffic Simulation via Scene-Level Diffusion | https://openreview.net/forum?id=nKWQnYkkwX | [
"Ziyuan Zhong",
"Davis Rempe",
"Yuxiao Chen",
"Boris Ivanovic",
"Yulong Cao",
"Danfei Xu",
"Marco Pavone",
"Baishakhi Ray"
] | Oral | null | Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling learning-based traffic models require significant domain expertise and are difficult for practitioners to use. To remedy this, we present CT... | [
"Traffic Simulation",
"Multi-Agent Diffusion",
"Large Language Model"
] | A scene-level conditional diffusion model with a LLM based language interface for realistic and controllable traffic simulation. | 174 | 2306.06344 | title_snapshot |
hRZ1YjDZmTo | MimicPlay: Long-Horizon Imitation Learning by Watching Human Play | https://openreview.net/forum?id=hRZ1YjDZmTo | [
"Chen Wang",
"Linxi Fan",
"Jiankai Sun",
"Ruohan Zhang",
"Li Fei-Fei",
"Danfei Xu",
"Yuke Zhu",
"Anima Anandkumar"
] | Oral | null | Imitation learning from human demonstrations is a promising paradigm for teaching robots manipulation skills in the real world. However, learning complex long-horizon tasks often requires an unattainable amount of demonstrations. To reduce the high data requirement, we resort to human play data - video sequences of peo... | [
"Imitation Learning",
"Learning from Human",
"Long-Horizon Manipulation"
] | We present MimicPlay, a novel imitation learning algorithm that leverages cost-effective human play data to learn long-horizon manipulation tasks in a sample-efficient manner. | 177 | 2302.12422 | title_snapshot |
yHlUVHWnBN | SCONE: A Food Scooping Robot Learning Framework with Active Perception | https://openreview.net/forum?id=yHlUVHWnBN | [
"Yen-Ling Tai",
"Yu Chien Chiu",
"Yu-Wei Chao",
"Yi-Ting Chen"
] | Poster | null | Effectively scooping food items poses a substantial challenge for current robotic systems, due to the intricate states and diverse physical properties of food. To address this challenge, we believe in the importance of encoding food items into meaningful representations for effective food scooping. However, the distinc... | [
"Food Manipulation",
"Robot Scooping",
"Active Perception"
] | A food scooping robot learning framework with active perception to improve food property-wise generalization. | 180 | null | null |
g_PPHV_GkX | Hierarchical Planning for Rope Manipulation using Knot Theory and a Learned Inverse Model | https://openreview.net/forum?id=g_PPHV_GkX | [
"Matan Sudry",
"Tom Jurgenson",
"Aviv Tamar",
"Erez Karpas"
] | Poster | null | This work considers planning the manipulation of deformable 1-dimensional objects, such as ropes or cables, specifically to tie knots. We propose TWISTED: Tying With Inverse model and Search in Topological space Excluding Demos, a hierarchical planning approach which, at the high level, uses ideas from knot-theory to p... | [
"Manipulation",
"Robot Learning and Planning"
] | Tying with Inverse model and search in topological space excluding demos | 182 | null | null |
0mRSANSzEK | Improving Behavioural Cloning with Positive Unlabeled Learning | https://openreview.net/forum?id=0mRSANSzEK | [
"Qiang Wang",
"Robert McCarthy",
"David Cordova Bulens",
"Kevin McGuinness",
"Noel E. O’Connor",
"Francisco Roldan Sanchez",
"Nico Gürtler",
"Felix Widmaier",
"Stephen J. Redmond"
] | Poster | null | Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore,... | [
"Offline policy learning",
"positive unlabeled learning",
"behavioural cloning"
] | null | 187 | 2301.11734 | title_snapshot |
GsM2qJTAg- | Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees | https://openreview.net/forum?id=GsM2qJTAg- | [
"Edoardo Caldarelli",
"Antoine Chatalic",
"Adrià Colomé",
"Lorenzo Rosasco",
"Carme Torras"
] | Poster | null | Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric models that can be used to infer nonlinear functions with time-varying noise. In robotics, they can be employed for learning from demonstration as motion primitives, i.e. as a model of the trajectories to be executed by the robot. HGPs provide v... | [
"Gaussian process regression",
"random features",
"motion primitives"
] | Heteroscedastic Gaussian processes and random features provide a scalable and provably accurate motion primitive for learning from demonstration. | 194 | null | null |
W8MjsxHrDpL | Synthesizing Navigation Abstractions for Planning with Portable Manipulation Skills | https://openreview.net/forum?id=W8MjsxHrDpL | [
"Eric Rosen",
"Steven James",
"Sergio Orozco",
"Vedant Gupta",
"Max Merlin",
"Stefanie Tellex",
"George Konidaris"
] | Poster | null | We address the problem of efficiently learning high-level abstractions for task-level robot planning. Existing approaches require large amounts of data and fail to generalize learned abstractions to new environments. To address this, we propose to exploit the independence between spatial and non-spatial state variabl... | [
"Learning Abstractions",
"Mobile Manipulation"
] | We present an efficient approach to learning transferable abstractions for task planning with mobile manipulators. | 195 | null | null |
AnDDMQgM7- | Equivariant Reinforcement Learning under Partial Observability | https://openreview.net/forum?id=AnDDMQgM7- | [
"Hai Huu Nguyen",
"Andrea Baisero",
"David Klee",
"Dian Wang",
"Robert Platt",
"Christopher Amato"
] | Poster | null | Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Specifically, by encoding the equivariance regarding specific... | [
"Partial Observability",
"Equivariant Learning",
"Symmetry"
] | This paper embeds domain symmetry in actor-critic reinforcement learning agents to solve a specific class of partially observable domains | 198 | 2408.14336 | title_snapshot |
keAPCON4jHC | Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization | https://openreview.net/forum?id=keAPCON4jHC | [
"Yuan Zhang",
"Jianhong Wang",
"Joschka Boedecker"
] | Poster | null | Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations, which excessively restricts its application for real-world robotics. Prior work claimed that adding regularization to the value function is equivalent to learning a robust policy under uncertain transit... | [
"Reinforcement Learning",
"Robustness",
"Continuous Control",
"Robotics"
] | null | 203 | 2207.02016 | title_snapshot |
dIgCPoy8E3 | Cross-Dataset Sensor Alignment: Making Visual 3D Object Detector Generalizable | https://openreview.net/forum?id=dIgCPoy8E3 | [
"Liangtao Zheng",
"Yicheng Liu",
"Yue Wang",
"Hang Zhao"
] | Poster | null | While camera-based 3D object detection has evolved rapidly, these models are susceptible to overfitting to specific sensor setups. For example, in autonomous driving, most datasets are collected using a single sensor configuration. This paper evaluates the generalization capability of camera-based 3D object detectors, ... | [
"3D object detection",
"Model Generalization",
"Autonomous Driving"
] | This paper intduces a method to improve the generalization capability of camera-based 3D object detectors for autonomous driving by aligning sensor configurations, resulting in significant performance improvements across multiple datasets. | 204 | null | null |
pw-OTIYrGa | On the Utility of Koopman Operator Theory in Learning Dexterous Manipulation Skills | https://openreview.net/forum?id=pw-OTIYrGa | [
"Yunhai Han",
"Mandy Xie",
"Ye Zhao",
"Harish Ravichandar"
] | Oral | null | Despite impressive dexterous manipulation capabilities enabled by learning-based approaches, we are yet to witness widespread adoption beyond well-resourced laboratories. This is likely due to practical limitations, such as significant computational burden, inscrutable learned behaviors, sensitivity to initialization, ... | [
"Koopman Operator",
"Dexterous Manipulation"
] | This paper investigates the utility of Koopman operator theory on dexterous manipulation tasks and reveals a number of unique benefits. | 208 | 2303.13446 | title_snapshot |
PK2debCKaG | Language Conditioned Traffic Generation | https://openreview.net/forum?id=PK2debCKaG | [
"Shuhan Tan",
"Boris Ivanovic",
"Xinshuo Weng",
"Marco Pavone",
"Philipp Kraehenbuehl"
] | Poster | null | Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in ren... | [
"Self-driving",
"Content generation",
"Large language model"
] | We propose a language-conditioned traffic scenario generation model powered by LLM. | 210 | 2307.07947 | title_snapshot |
low-53sFqn | Fleet Active Learning: A Submodular Maximization Approach | https://openreview.net/forum?id=low-53sFqn | [
"Oguzhan Akcin",
"Orhan Unuvar",
"Onat Ure",
"Sandeep P. Chinchali"
] | Poster | null | In multi-robot systems, robots often gather data to improve the performance of their deep neural networks (DNNs) for perception and planning. Ideally, these robots should select the most informative samples from their local data distributions by employing active learning approaches. However, when the data collection is... | [
"Active Learning",
"Cloud Robotics",
"Robotic Perception"
] | This work introduces a fleet active learning framework for multi-robot systems. | 213 | null | null |
dxOaNO8bge | A Data-Efficient Visual-Audio Representation with Intuitive Fine-tuning for Voice-Controlled Robots | https://openreview.net/forum?id=dxOaNO8bge | [
"Peixin Chang",
"Shuijing Liu",
"Tianchen Ji",
"Neeloy Chakraborty",
"Kaiwen Hong",
"Katherine Rose Driggs-Campbell"
] | Poster | null | A command-following robot that serves people in everyday life must continually improve itself in deployment domains with minimal help from its end users, instead of engineers. Previous methods are either difficult to continuously improve after the deployment or require a large number of new labels during fine-tuning. M... | [
"Command Following",
"Multimodal Representation",
"Reinforcement Learning",
"Human-in-the-Loop"
] | null | 217 | 2301.09749 | title_snapshot |
rOCWUmMBSnH | A Policy Optimization Method Towards Optimal-time Stability | https://openreview.net/forum?id=rOCWUmMBSnH | [
"Shengjie Wang",
"Lan Fengb",
"Xiang Zheng",
"Yuxue Cao",
"Oluwatosin OluwaPelumi Oseni",
"Haotian Xu",
"Tao Zhang",
"Yang Gao"
] | Poster | null | In current model-free reinforcement learning (RL) algorithms, stability criteria based on sampling methods are commonly utilized to guide policy optimization. However, these criteria only guarantee the infinite-time convergence of the system's state to an equilibrium point, which leads to sub-optimality of the policy. ... | [
"Reinforcement Learning",
"Robotic Control",
"Stability"
] | null | 218 | 2301.00521 | title_snapshot |
EvuAJ0wD98 | Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning | https://openreview.net/forum?id=EvuAJ0wD98 | [
"Xiyang Wu",
"Rohan Chandra",
"Tianrui Guan",
"Amrit Bedi",
"Dinesh Manocha"
] | Oral | null | Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we introduce a distributed multi-agent reinforcement learning (MARL) algorithm for joint trajectory and... | [
"Autonomous Driving",
"Multi-agent Reinforcement Learning",
"Representation Learning"
] | We combine opponent modeling, classical trajectory forecasting, and distributed MARL and show good performance both in avg. episodic reward and success rate on the (heterogeneous versions of) Highway-env and non-cooperative MPE. | 223 | 2306.06236 | title_judge |
Eyb4e3GBuuR | Energy-based Potential Games for Joint Motion Forecasting and Control | https://openreview.net/forum?id=Eyb4e3GBuuR | [
"Christopher Diehl",
"Tobias Klosek",
"Martin Krueger",
"Nils Murzyn",
"Timo Osterburg",
"Torsten Bertram"
] | Poster | null | This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control. Despite its interpretability, applying game theory to real-world robotics, like automated driving, faces challenges such as unknown game parameters. To tackle these, we establish a conne... | [
"Trajectory Prediction",
"Multi-Agent Interaction",
"Game-Theoretic Motion Planning",
"Energy-based Model",
"Optimal Control",
"Autonomous Vehicles"
] | A connection between differential games, optimal control, and energy-based models and an application for multi-agent motion forecasting and control, combining neural networks with differentiable game-theoretic optimization. | 226 | 2312.01811 | title_snapshot |
uJqxFjF1xWp | BM2CP: Efficient Collaborative Perception with LiDAR-Camera Modalities | https://openreview.net/forum?id=uJqxFjF1xWp | [
"Binyu Zhao",
"Wei ZHANG",
"Zhaonian Zou"
] | Poster | null | Collaborative perception enables agents to share complementary perceptual information with nearby agents. This can significantly benefit the perception performance and alleviate the issues of single-view perception, such as occlusion and sparsity. Most proposed approaches mainly focus on single modality (especially LiD... | [
"Multi-Agent Perception",
"Multi-Modal Fusion",
"Vehicle-to-Everything (V2X) Application"
] | null | 228 | 2310.14702 | title_snapshot |
qVc7NWYTRZ6 | An Unbiased Look at Datasets for Visuo-Motor Pre-Training | https://openreview.net/forum?id=qVc7NWYTRZ6 | [
"Sudeep Dasari",
"Mohan Kumar Srirama",
"Unnat Jain",
"Abhinav Gupta"
] | Poster | null | Visual representation learning hold great promise for robotics, but is severely hampered by the scarcity and homogeneity of robotics datasets. Recent works address this problem by pre-training visual representations on large-scale but out-of-domain data (e.g., videos of egocentric interactions) and then transferring th... | [
"Visual Representation Learning",
"Datasets",
"Manipulation"
] | Our data-centric analysis busts some common myths in visuo-motor pre-training! We find that old-school datasets (e.g. ImageNet) outcompete SOTA robotics baselines (trained on 5x more Ego4D data), and offer simple guidelines for improving evaluation. | 231 | 2310.09289 | title_snapshot |
MF_cS7TCYk | Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors | https://openreview.net/forum?id=MF_cS7TCYk | [
"Sushant Veer",
"Apoorva Sharma",
"Marco Pavone"
] | Poster | null | Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn ... | [
"trajectory prediction",
"rule-based planning"
] | We present an approach that combines learning- and rule-based trajectory predictors via recursive Bayesian filtering and demonstrate its ability to deliver more consistent performance than the standalone predictors. | 233 | 2307.01408 | title_snapshot |
fNLBmtyBiC | A Bayesian approach to breaking things: efficiently predicting and repairing failure modes via sampling | https://openreview.net/forum?id=fNLBmtyBiC | [
"Charles Dawson",
"Chuchu Fan"
] | Poster | null | Before autonomous systems can be deployed in safety-critical applications, we must be able to understand and verify the safety of these systems. For cases where the risk or cost of real-world testing is prohibitive, we propose a simulation-based framework for a) predicting ways in which an autonomous system is likely t... | [
"Automatic design tools",
"root-cause failure analysis",
"optimization-as-inference"
] | By reframing adversarial optimization as Bayesian inference, we can efficiently predict and repair failure modes in autonomous systems. | 237 | 2309.08052 | title_snapshot |
_A15qsPswaK | HYDRA: Hybrid Robot Actions for Imitation Learning | https://openreview.net/forum?id=_A15qsPswaK | [
"Suneel Belkhale",
"Yuchen Cui",
"Dorsa Sadigh"
] | Poster | null | Imitation Learning (IL) is a sample efficient paradigm for robot learning using expert demonstrations. However, policies learned through IL suffer from state distribution shift at test time, due to compounding errors in action prediction which lead to previously unseen states. Choosing an action representation for the ... | [
"Imitation Learning",
"Robotics",
"Manipulation"
] | We learn a hybrid robot action space that dynamically switches between low-level actions and high-level waypoints, and our method substantially outperforms baselines on a variety of long horizon real world tasks like making coffee and sorting dishes. | 238 | 2306.17237 | title_snapshot |
BzjLaVvr955 | Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning | https://openreview.net/forum?id=BzjLaVvr955 | [
"Jianxiang Feng",
"Jongseok Lee",
"Simon Geisler",
"Stephan Günnemann",
"Rudolph Triebel"
] | Poster | null | To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex targe... | [
"Normalizing Flows",
"Out-of-Distribution Detection",
"Robotic Introspection"
] | We propose to equip NFs with efficient but flexible base distributions to overcome the topological constraint for OOD detection in robot learning. | 241 | 2311.06481 | title_snapshot |
yGkqN4hqrJ | Fine-Tuning Generative Models as an Inference Method for Robotic Tasks | https://openreview.net/forum?id=yGkqN4hqrJ | [
"Orr Krupnik",
"Elisei Shafer",
"Tom Jurgenson",
"Aviv Tamar"
] | Poster | null | Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to evidence, we build on recent advances in deep generative models which have greatly af... | [
"robotic learning",
"fine-tuning",
"generative models"
] | A simple and general method for quickly fine-tuning generative models to novel observations in robotic tasks. | 242 | 2310.12862 | title_snapshot |
VLihM67Wdi6 | STERLING: Self-Supervised Terrain Representation Learning from Unconstrained Robot Experience | https://openreview.net/forum?id=VLihM67Wdi6 | [
"Haresh Karnan",
"Elvin Yang",
"Daniel Farkash",
"Garrett Warnell",
"Joydeep Biswas",
"Peter Stone"
] | Poster | null | Terrain awareness, i.e., the ability to identify and distinguish different types of terrain, is a critical ability that robots must have to succeed at autonomous off-road navigation. Current approaches that provide robots with this awareness either rely on labeled data which is expensive to collect, engineered features... | [
"Vision-based Navigation",
"Representation Learning",
"Learning from Experience"
] | We propose a novel self-supervised terrain representation learning algorithm that can learn relevant representations from unconstrained, unlabelled robot experience. We evaluate it against competitve baselines on a real robot in outdoor environments. | 243 | 2309.15302 | title_snapshot |
efaE7iJ2GJv | PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation | https://openreview.net/forum?id=efaE7iJ2GJv | [
"Shizhe Chen",
"Ricardo Garcia Pinel",
"Cordelia Schmid",
"Ivan Laptev"
] | Poster | null | The ability for robots to comprehend and execute manipulation tasks based on natural language instructions is a long-term goal in robotics. The dominant approaches for language-guided manipulation use 2D image representations, which face difficulties in combining multi-view cameras and inferring precise 3D positions an... | [
"Robotic manipulation",
"3D point clouds",
"language-guided policy"
] | null | 247 | 2309.15596 | title_snapshot |
VtUZ4VGPns | IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors | https://openreview.net/forum?id=VtUZ4VGPns | [
"Gaurav Datta",
"Ryan Hoque",
"Anrui Gu",
"Eugen Solowjow",
"Ken Goldberg"
] | Poster | null | Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distribution shift by allowing robots to access remote human supervisors during task exe... | [
"Imitation Learning",
"Fleet Learning",
"Energy-Based Models"
] | A novel imitation learning algorithm for handling both distribution shift and multimodality. | 248 | 2306.15228 | title_snapshot |
4ZK8ODNyFXx | Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners | https://openreview.net/forum?id=4ZK8ODNyFXx | [
"Allen Z. Ren",
"Anushri Dixit",
"Alexandra Bodrova",
"Sumeet Singh",
"Stephen Tu",
"Noah Brown",
"Peng Xu",
"Leila Takayama",
"Fei Xia",
"Jake Varley",
"Zhenjia Xu",
"Dorsa Sadigh",
"Andy Zeng",
"Anirudha Majumdar"
] | Oral | null | Large language models (LLMs) exhibit a wide range of promising capabilities --- from step-by-step planning to commonsense reasoning --- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, a framework for measuring and aligning the uncertainty o... | [
"Language-based planning",
"uncertainty estimation",
"conformal prediction"
] | null | 249 | 2307.01928 | title_snapshot |
j2AQ-WJ_ze | Language-guided Robot Grasping: CLIP-based Referring Grasp Synthesis in Clutter | https://openreview.net/forum?id=j2AQ-WJ_ze | [
"Georgios Tziafas",
"Yucheng XU",
"Arushi Goel",
"Mohammadreza Kasaei",
"Zhibin Li",
"Hamidreza Kasaei"
] | Poster | null | Robots operating in human-centric environments require the integration of visual grounding and grasping capabilities to effectively manipulate objects based on user instructions. This work focuses on the task of referring grasp synthesis, which predicts a grasp pose for an object referred through natural language in cl... | [
"Language-Guided Robot Grasping",
"Referring Grasp Synthesis",
"Visual Grounding"
] | A new benchmark and an end-to-end model for language-guided 4-DoF grasp synthesis in cluttered tabletop scenes. | 251 | 2311.05779 | title_snapshot |
4uFVn6WHyzo | Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering | https://openreview.net/forum?id=4uFVn6WHyzo | [
"Yasasa Abeysirigoonawardena",
"Kevin Xie",
"Chuhan Chen",
"Salar Hosseini Khorasgani",
"Ruiting Chen",
"Ruiqi Wang",
"Florian Shkurti"
] | Poster | null | Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance t... | [
"robotics",
"adversarial attacks",
"simulation"
] | Adversarial attacks for autonomous driving using differentiable surrogate simulators | 253 | 2309.15770 | title_snapshot |
QG_ERxtDAP- | Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks | https://openreview.net/forum?id=QG_ERxtDAP- | [
"Clemens Schwarke",
"Victor Klemm",
"Matthijs van der Boon",
"Marko Bjelonic",
"Marco Hutter"
] | Poster | null | Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper proposes an intrinsically motivated RL approach to reduce task-specifi... | [
"Curiosity",
"Reinforcement Learning",
"Wheeled-Legged Robots"
] | An intrinsically motivated RL approach that simplifies learning complex robotics tasks, such as door opening, is proposed and validated. | 257 | null | null |
9al6taqfTzr | Open-World Object Manipulation using Pre-Trained Vision-Language Models | https://openreview.net/forum?id=9al6taqfTzr | [
"Austin Stone",
"Ted Xiao",
"Yao Lu",
"Keerthana Gopalakrishnan",
"Kuang-Huei Lee",
"Quan Vuong",
"Paul Wohlhart",
"Sean Kirmani",
"Brianna Zitkovich",
"Fei Xia",
"Chelsea Finn",
"Karol Hausman"
] | Poster | null | For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary, e.g. ``can you get me the pink stuffed whale?'' to their sensory observations and actions. This brings up a notably difficult challenge for robots: while robot learning approaches allow robots ... | [] | null | 259 | 2303.00905 | title_snapshot |
rvh0vkwKUM | Predicting Routine Object Usage for Proactive Robot Assistance | https://openreview.net/forum?id=rvh0vkwKUM | [
"Maithili Patel",
"Aswin Gururaj Prakash",
"Sonia Chernova"
] | Poster | null | Proactivity in robot assistance refers to the robot's ability to anticipate user needs and perform assistive actions without explicit requests. This requires understanding user routines, predicting consistent activities, and actively seeking information to predict inconsistent behaviors. We propose SLaTe-PRO (Sequentia... | [
"Proactive Robot Assistance",
"User Routine Understanding",
"Interactive Clarification",
"Robot Learning"
] | We propose SLaTe-PRO, a model which can learn to anticipate user's needs from past observations and use them to provide proactive assistance, and an interactive clarification mechanism, which can further refine such predictions. | 260 | 2309.06252 | title_snapshot |
C5MQUlzhVjQ | Surrogate Assisted Generation of Human-Robot Interaction Scenarios | https://openreview.net/forum?id=C5MQUlzhVjQ | [
"Varun Bhatt",
"Heramb Nemlekar",
"Matthew Christopher Fontaine",
"Bryon Tjanaka",
"Hejia Zhang",
"Ya-Chuan Hsu",
"Stefanos Nikolaidis"
] | Oral | null | As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmically generated diverse scenarios that reveal system failures in a shar... | [
"Scenario Generation",
"Human-Robot Interaction",
"Quality Diversity"
] | We present an efficient method of automatically generating challenging and diverse human-robot interaction scenarios. | 261 | 2304.13787 | title_snapshot |
fvXFBCHVGn | Dynamic Multi-Team Racing: Competitive Driving on 1/10-th Scale Vehicles via Learning in Simulation | https://openreview.net/forum?id=fvXFBCHVGn | [
"Peter Werner",
"Tim Seyde",
"Paul Drews",
"Thomas Matrai Balch",
"Igor Gilitschenski",
"Wilko Schwarting",
"Guy Rosman",
"Sertac Karaman",
"Daniela Rus"
] | Poster | null | Autonomous racing is a challenging task that requires vehicle handling at the dynamic limits of friction. While single-agent scenarios like Time Trials are solved competitively with classical model-based or model-free feedback control, multi-agent wheel-to-wheel racing poses several challenges including planning over u... | [
"Multi-Agent",
"Reinforcement Learning",
"Sim-to-Real Transfer",
"Autonomous Racing"
] | We learn dynamic 2-vs-2 multi-team racing via hierarchical policies through self-play reinforcement learning, and demonstrate that the competitive emergent behavior transfers to hardware. | 263 | null | null |
-HFJuX1uqs | Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation | https://openreview.net/forum?id=-HFJuX1uqs | [
"Theophile Gervet",
"Zhou Xian",
"Nikolaos Gkanatsios",
"Katerina Fragkiadaki"
] | Poster | null | 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to pro... | [
"Learning from Demonstrations",
"Manipulation",
"Transformers"
] | We introduce Act3D, a manipulation policy transformer that represents the robot’s workspace using a 3D feature field with adaptive resolutions. | 264 | 2306.17817 | title_snapshot |
VscdYkKgwdH | Neural Graph Control Barrier Functions Guided Distributed Collision-avoidance Multi-agent Control | https://openreview.net/forum?id=VscdYkKgwdH | [
"Songyuan Zhang",
"Kunal Garg",
"Chuchu Fan"
] | Poster | null | We consider the problem of designing distributed collision-avoidance multi-agent control in large-scale environments with potentially moving obstacles, where a large number of agents are required to maintain safety using only local information and reach their goals. This paper addresses the problem of collision avoidan... | [
"Distributed control",
"Control barrier functions",
"Graph neural networks"
] | We introduce a new notion of GCBF to encode inter-agent collision and obstacle avoidance in control for large-scale multi-agent systems with LiDAR-based observations, and jointly learn it with a distributed controller using GNNs. | 268 | 2311.13014 | title_snapshot |
wMpOMO0Ss7a | SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planning | https://openreview.net/forum?id=wMpOMO0Ss7a | [
"Krishan Rana",
"Jesse Haviland",
"Sourav Garg",
"Jad Abou-Chakra",
"Ian Reid",
"Niko Suenderhauf"
] | Oral | null | Large language models (LLMs) have demonstrated impressive results in developing generalist planning agents for diverse tasks. However, grounding these plans in expansive, multi-floor, and multi-room environments presents a significant challenge for robotics. We introduce SayPlan, a scalable approach to LLM-based, large... | [
"robot task planning",
"large language models",
"semantic search",
"LLM-based planning",
"3D scene graphs"
] | Combining large language models and 3D Scene Graphs for scalable robotic task planning in expansive multi-floor and multi-room environments, using semantic search and iterative re-planning. | 269 | 2307.06135 | title_snapshot |
Pwsm7d0iWJD | Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation | https://openreview.net/forum?id=Pwsm7d0iWJD | [
"Amin Abyaneh",
"Hsiu-Chin Lin"
] | Poster | null | Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided util... | [
"Imitation learning",
"Safe learning",
"Motion planning",
"Dynamical system",
"Semidefinite programming",
"Robotic manipulation"
] | This research presents an approach to stable motion planning from expert's demonstrations by jointly learning a dynamical system and Lyapunov candidate through constrained polynomial regression. | 274 | 2310.20605 | title_snapshot |
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