CoRL
Collection
Accepted papers for CoRL (Conference on Robot Learning), one dataset per year. • 10 items • Updated
paper_id stringlengths 10 20 | title stringlengths 26 134 | paper_url stringlengths 45 55 | authors listlengths 1 32 | type stringclasses 0
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values | abstract large_stringlengths 502 2.19k | keywords listlengths 0 0 | TL;DR large_stringclasses 0
values | submission_number int64 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
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v155/huang21a | Learning a Decision Module by Imitating Driver’s Control Behaviors | https://proceedings.mlr.press/v155/huang21a.html | [
"Junning Huang",
"Sirui Xie",
"Jiankai Sun",
"Qiurui Ma",
"Chunxiao Liu",
"Dahua Lin",
"Bolei Zhou"
] | null | null | Autonomous driving systems have a pipeline of perception, decision, planning, and control. The decision module processes information from the perception module and directs the execution of downstream planning and control modules. On the other hand, the recent success of deep learning suggests that this pipeline could b... | [] | null | null | 1912.00191 | title_snapshot |
v155/weng21a | Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting | https://proceedings.mlr.press/v155/weng21a.html | [
"Xinshuo Weng",
"Jianren Wang",
"Sergey Levine",
"Kris Kitani",
"Nicholas Rhinehart"
] | null | null | Many autonomous systems forecast aspects of the future in order to aid decision-making. For example, self-driving vehicles and robotic manipulation systems often forecast future object poses by first detecting and tracking objects. However, this detect-then-forecast pipeline is expensive to scale, as pose forecasting a... | [] | null | null | 2003.08376 | title_snapshot |
v155/sun21a | Neuro-Symbolic Program Search for Autonomous Driving Decision Module Design | https://proceedings.mlr.press/v155/sun21a.html | [
"Jiankai Sun",
"Hao Sun",
"Tian Han",
"Bolei Zhou"
] | null | null | As a promising topic in cognitive robotics, neuro-symbolic modeling integrates symbolic reasoning and neural representation altogether. However, previous neuro-symbolic models usually wire their structures and the connections manually, making the underlying parameters sub-optimal. In this work, we propose the Neuro-Sym... | [] | null | null | null | null |
v155/shah21a | LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion | https://proceedings.mlr.press/v155/shah21a.html | [
"Meet Shah",
"Zhiling Huang",
"Ankit Laddha",
"Matthew Langford",
"Blake Barber",
"sida zhang",
"Carlos Vallespi-Gonzalez",
"Raquel Urtasun"
] | null | null | In this paper, we present LiRaNet, a novel end-to-end trajectory prediction method which utilizes radar sensor information along with widely used lidar and HD maps. Automotive radar provides rich, complementary information, allowing for longer range vehicle detection as well as instantaneous radial velocity measurement... | [] | null | null | 2010.00731 | title_snapshot |
v155/choi21a | DROGON: A Trajectory Prediction Model based on Intention-Conditioned Behavior Reasoning | https://proceedings.mlr.press/v155/choi21a.html | [
"Chiho Choi",
"Srikanth Malla",
"Abhishek Patil",
"Joon Hee Choi"
] | null | null | We propose a Deep RObust Goal-Oriented trajectory prediction Network (DROGON) for accurate vehicle trajectory prediction by considering behavioral intentions of vehicles in traffic scenes. Our main insight is that the behavior (i.e., motion) of drivers can be reasoned from their high level possible goals (i.e., intenti... | [] | null | null | 1908.00024 | title_snapshot |
v155/chitnis21a | CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs | https://proceedings.mlr.press/v155/chitnis21a.html | [
"Rohan Chitnis",
"Tom Silver",
"Beomjoon Kim",
"Leslie Kaelbling",
"Tomas Lozano-Perez"
] | null | null | Meta-planning, or learning to guide planning from experience, is a promising approach to improving the computational cost of planning. A general meta-planning strategy is to learn to impose constraints on the states considered and actions taken by the agent. We observe that (1) imposing a constraint can induce context-... | [] | null | null | 2007.13202 | title_snapshot |
v155/jena21a | Augmenting GAIL with BC for sample efficient imitation learning | https://proceedings.mlr.press/v155/jena21a.html | [
"Rohit Jena",
"Changliu Liu",
"Katia Sycara"
] | null | null | Imitation learning is the problem of recovering an expert policy without access to a reward signal. Behavior cloning and GAIL are two widely used methods for performing imitation learning. Behavior cloning converges in a few iterations, but doesn’t achieve peak performance due to its inherent iid assumption about the s... | [] | null | null | 2001.07798 | title_snapshot |
v155/jain21a | From pixels to legs: Hierarchical learning of quadruped locomotion | https://proceedings.mlr.press/v155/jain21a.html | [
"Deepali Jain",
"Ken Caluwaerts",
"Atil Iscen"
] | null | null | Legged robots navigating crowded scenes and complex terrains in the real world are required to execute dynamic leg movements while processing visual input for obstacle avoidance and path planning. We show that a quadruped robot can acquire both of these skills by means of hierarchical reinforcement learning (HRL). By v... | [] | null | null | 2011.11722 | title_snapshot |
v155/ha21a | Learning a Decentralized Multi-Arm Motion Planner | https://proceedings.mlr.press/v155/ha21a.html | [
"Huy Ha",
"Jingxi Xu",
"Shuran Song"
] | null | null | We present a closed-loop multi-arm motion planner that is scalable and flexible with team size. Traditional multi-arm robotic systems have relied on centralized motion planners, whose run times often scale exponentially with team size, and thus, fail to handle dynamic environments with open-loop control. In this paper,... | [] | null | null | 2011.02608 | title_snapshot |
v155/xu21a | SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks | https://proceedings.mlr.press/v155/xu21a.html | [
"Yan Xu",
"Zhaoyang Huang",
"Kwan-Yee Lin",
"Xinge Zhu",
"Jianping Shi",
"Hujun Bao",
"Guofeng Zhang",
"Hongsheng Li"
] | null | null | Recent learning-based LiDAR odometry methods have demonstrated their competitiveness. However, most methods still face two substantial challenges: 1) the 2D projection representation of LiDAR data cannot effectively encode 3D structures from the point clouds; 2) the needs for a large amount of labeled data for training... | [] | null | null | 2010.09343 | title_snapshot |
v155/xu21b | Learning 3D Dynamic Scene Representations for Robot Manipulation | https://proceedings.mlr.press/v155/xu21b.html | [
"Zhenjia Xu",
"Zhanpeng He",
"Jiajun Wu",
"Shuran Song"
] | null | null | 3D scene representation for robot manipulation should capture three key object properties: permanency - objects that become occluded over time continue to exist; amodal completeness - objects have 3D occupancy, even if only partial observations are available; spatiotemporal continuity - the movement of each object is c... | [] | null | null | 2011.01968 | title_snapshot |
v155/wang21a | CoT-AMFlow: Adaptive Modulation Network with Co-Teaching Strategy for Unsupervised Optical Flow Estimation | https://proceedings.mlr.press/v155/wang21a.html | [
"Hengli Wang",
"Rui Fan",
"Ming Liu"
] | null | null | The interpretation of ego motion and scene change is a fundamental task for mobile robots. Optical flow information can be employed to estimate motion in the surroundings. Recently, unsupervised optical flow estimation has become a research hotspot. However, unsupervised approaches are often easy to be unreliable on p... | [] | null | null | 2011.02156 | title_snapshot |
v155/zhao21a | SAM: Squeeze-and-Mimic Networks for Conditional Visual Driving Policy Learning | https://proceedings.mlr.press/v155/zhao21a.html | [
"Albert Zhao",
"Tong He",
"Yitao Liang",
"Haibin Huang",
"Guy Van den Broeck",
"Stefano Soatto"
] | null | null | We describe a policy learning approach to map visual inputs to driving controls conditioned on turning command that leverages side tasks on semantics and object affordances via a learned representation trained for driving. To learn this representation, we train a squeeze network to drive using annotations for the side ... | [] | null | null | 1912.02973 | title_snapshot |
v155/ha21b | Fit2Form: 3D Generative Model for Robot Gripper Form Design | https://proceedings.mlr.press/v155/ha21b.html | [
"Huy Ha",
"Shubham Agrawal",
"Shuran Song"
] | null | null | The 3D shape of a robot’s end-effector plays a critical role in determining it’s functionality and overall performance. Many of today’s industrial applications rely on highly customized gripper design for a given task to ensure the system’s robustness and accuracy. However, the process of manual hardware design is bot... | [] | null | null | 2011.06498 | title_snapshot |
v155/pertsch21a | Accelerating Reinforcement Learning with Learned Skill Priors | https://proceedings.mlr.press/v155/pertsch21a.html | [
"Karl Pertsch",
"Youngwoon Lee",
"Joseph Lim"
] | null | null | Intelligent agents rely heavily on prior experience when learning a new task, yet most modern reinforcement learning (RL) approaches learn every task from scratch. One approach for leveraging prior knowledge is to transfer skills learned on prior tasks to the new task. However, as the amount of prior experience increas... | [] | null | null | 2010.11944 | title_snapshot |
v155/xu21c | Positive-Unlabeled Reward Learning | https://proceedings.mlr.press/v155/xu21c.html | [
"Danfei Xu",
"Misha Denil"
] | null | null | Learning reward functions from data is a promising path towards achieving scalable Reinforcement Learning (RL) for robotics. However, a major challenge in training agents from learned reward models is that the agent can learn to exploit errors in the reward model to achieve high reward behaviors that do not correspond ... | [] | null | null | 1911.00459 | title_snapshot |
v155/jeng21a | GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection | https://proceedings.mlr.press/v155/jeng21a.html | [
"Kuang-Yu Jeng",
"Yueh-Cheng Liu",
"Zhe Yu Liu",
"Jen-Wei Wang",
"Ya-Liang Chang",
"Hung-Ting Su",
"Winston Hsu"
] | null | null | We proposed an end-to-end grasp detection network, Grasp Detection Network (GDN), cooperated with a novel coarse-to-fine (C2F) grasp representation design to detect diverse and accurate 6-DoF grasps based on point clouds. Compared to previous two-stage approaches which sample and evaluate multiple grasp candidates, ... | [] | null | null | 2010.10695 | title_snapshot |
v155/xiao21a | Action-based Representation Learning for Autonomous Driving | https://proceedings.mlr.press/v155/xiao21a.html | [
"Yi Xiao",
"Felipe Codevilla",
"Christopher Pal",
"Antonio Lopez"
] | null | null | Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically ... | [] | null | null | 2008.09417 | title_snapshot |
v155/zolna21a | Task-Relevant Adversarial Imitation Learning | https://proceedings.mlr.press/v155/zolna21a.html | [
"Konrad Zolna",
"Scott Reed",
"Alexander Novikov",
"Sergio Gómez Colmenarejo",
"David Budden",
"Serkan Cabi",
"Misha Denil",
"Nando de Freitas",
"Ziyu Wang"
] | null | null | We show that a critical vulnerability in adversarial imitation is the tendency of discriminator networks to learn spurious associations between visual features and expert labels. When the discriminator focuses on task-irrelevant features, it does not provide an informative reward signal, leading to poor task performanc... | [] | null | null | 1910.01077 | title_snapshot |
v155/zhou21a | SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving | https://proceedings.mlr.press/v155/zhou21a.html | [
"Ming Zhou",
"Jun Luo",
"Julian Villella",
"Yaodong Yang",
"David Rusu",
"Jiayu Miao",
"Weinan Zhang",
"Montgomery Alban",
"IMAN FADAKAR",
"Zheng Chen",
"Chongxi Huang",
"Ying Wen",
"Kimia Hassanzadeh",
"Daniel Graves",
"Zhengbang Zhu",
"Yihan Ni",
"Nhat Nguyen",
"Mohamed Elsayed",... | null | null | Interaction is fundamental in autonomous driving (AD). Despite more than a decade of intensive R&D in AD, how to dynamically interact with diverse road users in various contexts still remains unsolved. Multi-agent learning has recently seen big breakthroughs and has much to offer towards solving realistic interaction i... | [] | null | null | 2010.09776 | title_judge |
v155/wang21b | Reconfigurable Voxels: A New Representation for LiDAR-Based Point Clouds | https://proceedings.mlr.press/v155/wang21b.html | [
"Tai Wang",
"Xinge Zhu",
"Dahua Lin"
] | null | null | LiDAR is an important method for autonomous driving systems to sense the environment. The point clouds obtained by LiDAR typically exhibit sparse and irregular distribution, thus posing great challenges to the detection of 3D objects, especially those that are small and distant. To tackle this difficulty, we propose Re... | [] | null | null | 2004.02724 | title_snapshot |
v155/petrik21a | Learning Object Manipulation Skills via Approximate State Estimation from Real Videos | https://proceedings.mlr.press/v155/petrik21a.html | [
"Vladimír Petrík",
"Makarand Tapaswi",
"Ivan Laptev",
"Josef Sivic"
] | null | null | Humans are adept at learning new tasks by watching a few instructional videos. On the other hand, robots that learn new actions either require a lot of effort through trial and error, or use expert demonstrations that are challenging to obtain. In this paper, we explore a method that facilitates learning object manipul... | [] | null | null | 2011.06813 | title_snapshot |
v155/datta21a | Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents | https://proceedings.mlr.press/v155/datta21a.html | [
"Samyak Datta",
"Oleksandr Maksymets",
"Judy Hoffman",
"Stefan Lee",
"Dhruv Batra",
"Devi Parikh"
] | null | null | Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with idealized sensors for localization and take deterministic actions. This setting is practically sterile by comparison to the dirty reality of ... | [] | null | null | 2009.03231 | title_snapshot |
v155/buhet21a | PLOP: Probabilistic Polynomial Objects trajectory Prediction for autonomous driving | https://proceedings.mlr.press/v155/buhet21a.html | [
"Thibault Buhet",
"Emilie Wirbel",
"Andrei Bursuc",
"Xavier Perrotton"
] | null | null | To navigate safely in urban environments, an autonomous vehicle (ego vehicle) must understand and anticipate its surroundings, in particular the behavior and intents of other road users (neighbors). Most of the times, multiple decision choices are acceptable for all road users (e.g., turn right or left, or different wa... | [] | null | null | 2003.08744 | title_judge |
v155/schmeckpeper21a | Reinforcement Learning with Videos: Combining Offline Observations with Interaction | https://proceedings.mlr.press/v155/schmeckpeper21a.html | [
"Karl Schmeckpeper",
"Oleh Rybkin",
"Kostas Daniilidis",
"Sergey Levine",
"Chelsea Finn"
] | null | null | Reinforcement learning is a powerful framework for robots to acquire skills from experience, but often requires a substantial amount of online data collection. As a result, it is difficult to collect sufficiently diverse experiences that are needed for robots to generalize broadly. Videos of humans, on the other hand, ... | [] | null | null | 2011.06507 | title_snapshot |
v155/strudel21a | Learning Obstacle Representations for Neural Motion Planning | https://proceedings.mlr.press/v155/strudel21a.html | [
"Robin STRUDEL",
"Ricardo Garcia Pinel",
"Justin Carpentier",
"Jean-Paul Laumond",
"Ivan Laptev",
"Cordelia Schmid"
] | null | null | Motion planning and obstacle avoidance is a key challenge in robotics applications. While previous work succeeds to provide excellent solutions for known environments, sensor-based motion planning in new and dynamic environments remains to be difficult. In this work we address sensor-based motion planning from a learn... | [] | null | null | 2008.11174 | title_snapshot |
v155/wang21c | CLOUD: Contrastive Learning of Unsupervised Dynamics | https://proceedings.mlr.press/v155/wang21c.html | [
"Jianren Wang",
"Yujie Lu",
"Hang Zhao"
] | null | null | Developing agents that can perform complex control tasks from high dimensional observations such as pixels is challenging due to difficulties in learning dynamics efficiently. In this work, we propose to learn forward and inverse dynamics in a fully unsupervised manner via contrastive estimation. Specifically, we train... | [] | null | null | 2010.12488 | title_snapshot |
v155/danielczuk21a | Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects | https://proceedings.mlr.press/v155/danielczuk21a.html | [
"Michael Danielczuk",
"Ashwin Balakrishna",
"Daniel Brown",
"Ken Goldberg"
] | null | null | There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consistently fail to grasp challenging objects which are significantly out of the distribution of objects in the training data or which have very few high quality grasps. Motivat... | [] | null | null | 2011.05632 | title_snapshot |
v155/salter21a | Attention-Privileged Reinforcement Learning | https://proceedings.mlr.press/v155/salter21a.html | [
"Sasha Salter",
"Dushyant Rao",
"Markus Wulfmeier",
"Raia Hadsell",
"Ingmar Posner"
] | null | null | Image-based Reinforcement Learning is known to suffer from poor sample efficiency and generalisation to unseen visuals such as distractors (task-independent aspects of the observation space). Visual domain randomisation encourages transfer by training over visual factors of variation that may be encountered in the targ... | [] | null | null | 1911.08363 | title_snapshot |
v155/houston21a | One Thousand and One Hours: Self-driving Motion Prediction Dataset | https://proceedings.mlr.press/v155/houston21a.html | [
"John Houston",
"Guido Zuidhof",
"Luca Bergamini",
"Yawei Ye",
"Long Chen",
"Ashesh Jain",
"Sammy Omari",
"Vladimir Iglovikov",
"Peter Ondruska"
] | null | null | Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles along a fixed route in Palo Alto, California, over a four-month period. It consists of 170... | [] | null | null | 2006.14480 | title_snapshot |
v155/yang21a | Recovering and Simulating Pedestrians in the Wild | https://proceedings.mlr.press/v155/yang21a.html | [
"Ze Yang",
"Sivabalan Manivasagam",
"Ming Liang",
"Bin Yang",
"Wei-Chiu Ma",
"Raquel Urtasun"
] | null | null | Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on artists to create both 3D assets and their animations to generate a new scenario. This, however, does not scale. In contrast, we propose to r... | [] | null | null | 2011.08106 | title_snapshot |
v155/lin21a | SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation | https://proceedings.mlr.press/v155/lin21a.html | [
"Xingyu Lin",
"Yufei Wang",
"Jake Olkin",
"David Held"
] | null | null | Manipulating deformable objects has long been a challenge in robotics due to its high dimensional state representation and complex dynamics. Recent success in deep reinforcement learning provides a promising direction for learning to manipulate deformable objects with data driven methods. However, existing reinforcemen... | [] | null | null | 2011.07215 | title_snapshot |
v155/vecerik21a | S3K: Self-Supervised Semantic Keypoints for Robotic Manipulation via Multi-View Consistency | https://proceedings.mlr.press/v155/vecerik21a.html | [
"Mel Vecerik",
"Jean-Baptiste Regli",
"Oleg Sushkov",
"David Barker",
"Rugile Pevceviciute",
"Thomas Rothörl",
"Raia Hadsell",
"Lourdes Agapito",
"Jonathan Scholz"
] | null | null | A robot’s ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria, e.g. image reconstruction, smoothness in latent space, or usefulness for control, or else make use of large datasets annotated with specifi... | [] | null | null | 2009.14711 | title_snapshot |
v155/xiang21a | Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation | https://proceedings.mlr.press/v155/xiang21a.html | [
"Yu Xiang",
"Christopher Xie",
"Arsalan Mousavian",
"Dieter Fox"
] | null | null | Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature embeddings from synthetic data. A metric learning loss function is utiliz... | [] | null | null | 2007.15157 | title_snapshot |
v155/pirk21a | Modeling Long-horizon Tasks as Sequential Interaction Landscapes | https://proceedings.mlr.press/v155/pirk21a.html | [
"Soeren Pirk",
"Karol Hausman",
"Alexander Toshev",
"Mohi Khansari"
] | null | null | Task planning over long-time horizons is a challenging and open problem in robotics and its complexity grows exponentially with an increasing number of subtasks. In this paper we present a deep neural network that learns dependencies and transitions across subtasks solely from a set of demonstration videos. We represen... | [] | null | null | 2006.04843 | title_snapshot |
v155/goyal21a | PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards | https://proceedings.mlr.press/v155/goyal21a.html | [
"Prasoon Goyal",
"Scott Niekum",
"Raymond Mooney"
] | null | null | Reinforcement learning (RL), particularly in sparse reward settings, often requires prohibitively large numbers of interactions with the environment, thereby limiting its applicability to complex problems. To address this, several prior approaches have used natural language to guide the agent’s exploration. However, th... | [] | null | null | 2007.15543 | title_snapshot |
v155/ye21a | Auxiliary Tasks Speed Up Learning Point Goal Navigation | https://proceedings.mlr.press/v155/ye21a.html | [
"Joel Ye",
"Dhruv Batra",
"Erik Wijmans",
"Abhishek Das"
] | null | null | PointGoal Navigation is an embodied task that requires agents to navigate to a specified point in an unseen environment. Wijmans et al. showed that this task is solvable in simulation but their method is computationally prohibitive - requiring 2.5 billion frames of experience and 180 GPU-days. We develop a method to si... | [] | null | null | 2007.04561 | title_judge |
v155/pal21a | Learning hierarchical relationships for object-goal navigation | https://proceedings.mlr.press/v155/pal21a.html | [
"Anwesan Pal",
"Yiding Qiu",
"Henrik Christensen"
] | null | null | Direct search for objects as part of navigation poses a challenge for small items. Utilizing context in the form of object-object relationships enable hierarchical search for targets efficiently. Most of the current approaches tend to directly incorporate sensory input into a reward-based learning approach, without lea... | [] | null | null | 2003.06749 | title_snapshot |
v155/ni21a | f-IRL: Inverse Reinforcement Learning via State Marginal Matching | https://proceedings.mlr.press/v155/ni21a.html | [
"Tianwei Ni",
"Harshit Sikchi",
"Yufei Wang",
"Tejus Gupta",
"Lisa Lee",
"Ben Eysenbach"
] | null | null | Imitation learning is well-suited for robotic tasks where it is difficult to directly program the behavior or specify a cost for optimal control. In this work, we propose a method for learning the reward function (and the corresponding policy) to match the expert state density. Our main result is the analytic gradient... | [] | null | null | 2011.04709 | title_snapshot |
v155/georgiev21a | Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing | https://proceedings.mlr.press/v155/georgiev21a.html | [
"Ignat Georgiev",
"Christoforos Chatzikomis",
"Timo Voelkl",
"Joshua Smith",
"Michael Mistry"
] | null | null | Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model Predictive Controller (MPC). We present a novel non-linear semi-parametric dynamics m... | [] | null | null | 2011.08750 | title_snapshot |
v155/yan21a | Learning Predictive Representations for Deformable Objects Using Contrastive Estimation | https://proceedings.mlr.press/v155/yan21a.html | [
"Wilson Yan",
"Ashwin Vangipuram",
"Pieter Abbeel",
"Lerrel Pinto"
] | null | null | Using visual model-based learning for deformable object manipulation is challenging due to difficulties in learning plannable visual representations along with complex dynamic models. In this work, we propose a new learning framework that jointly optimizes both the visual representation model and the dynamics model usi... | [] | null | null | 2003.05436 | title_snapshot |
v155/xie21a | Learning Latent Representations to Influence Multi-Agent Interaction | https://proceedings.mlr.press/v155/xie21a.html | [
"Annie Xie",
"Dylan Losey",
"Ryan Tolsma",
"Chelsea Finn",
"Dorsa Sadigh"
] | null | null | Seamlessly interacting with humans or robots is hard because these agents are non-stationary. They update their policy in response to the ego agent’s behavior, and the ego agent must anticipate these changes to co-adapt. Inspired by humans, we recognize that robots do not need to explicitly model every low-level action... | [] | null | null | 2011.06619 | title_snapshot |
v155/yamada21a | Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments | https://proceedings.mlr.press/v155/yamada21a.html | [
"Jun Yamada",
"Youngwoon Lee",
"Gautam Salhotra",
"Karl Pertsch",
"Max Pflueger",
"Gaurav Sukhatme",
"Joseph Lim",
"Peter Englert"
] | null | null | Deep reinforcement learning (RL) agents are able to learn contact-rich manipulation tasks by maximizing a reward signal, but require large amounts of experience, especially in environments with many obstacles that complicate exploration. In contrast, motion planners use explicit models of the agent and environment to p... | [] | null | null | 2010.11940 | title_snapshot |
v155/cui21a | The EMPATHIC Framework for Task Learning from Implicit Human Feedback | https://proceedings.mlr.press/v155/cui21a.html | [
"Yuchen Cui",
"Qiping Zhang",
"Brad Knox",
"Alessandro Allievi",
"Peter Stone",
"Scott Niekum"
] | null | null | Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. A robot or other agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This appr... | [] | null | null | 2009.13649 | title_snapshot |
v155/bewley21a | Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection | https://proceedings.mlr.press/v155/bewley21a.html | [
"Alex Bewley",
"Pei Sun",
"Thomas Mensink",
"Dragomir Anguelov",
"Cristian Sminchisescu"
] | null | null | This paper presents a novel 3D object detection framework that processes LiDAR data directly on its native representation: range images. Benefiting from the compactness of range images, 2D convolutions can efficiently process dense LiDAR data of the scene. To overcome scale sensitivity in this perspective view, a novel... | [] | null | null | 2005.09927 | title_snapshot |
v155/ploeger21a | High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards | https://proceedings.mlr.press/v155/ploeger21a.html | [
"Kai Ploeger",
"Michael Lutter",
"Jan Peters"
] | null | null | Robots that can learn in the physical world will be important to enable robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation withou... | [] | null | null | 2010.13483 | title_snapshot |
v155/dean21a | Guaranteeing Safety of Learned Perception Modules via Measurement-Robust Control Barrier Functions | https://proceedings.mlr.press/v155/dean21a.html | [
"Sarah Dean",
"Andrew Taylor",
"Ryan Cosner",
"Benjamin Recht",
"Aaron Ames"
] | null | null | Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to ... | [] | null | null | 2010.16001 | title_snapshot |
v155/anderson21a | Sim-to-Real Transfer for Vision-and-Language Navigation | https://proceedings.mlr.press/v155/anderson21a.html | [
"Peter Anderson",
"Ayush Shrivastava",
"Joanne Truong",
"Arjun Majumdar",
"Devi Parikh",
"Dhruv Batra",
"Stefan Lee"
] | null | null | We study the challenging problem of releasing a robot in a previously unseen environment, and having it follow unconstrained natural language navigation instructions. Recent work on the task of Vision-and-Language Navigation (VLN) has achieved significant progress in simulation. To assess the implications of this work ... | [] | null | null | 2011.03807 | title_snapshot |
v155/jauhri21a | Interactive Imitation Learning in State-Space | https://proceedings.mlr.press/v155/jauhri21a.html | [
"Snehal Jauhri",
"Carlos Celemin",
"Jens Kober"
] | null | null | Imitation Learning techniques enable programming the behaviour of agents through demonstrations rather than manual engineering. However, they are limited by the quality of available demonstration data. Interactive Imitation Learning techniques can improve the efficacy of learning since they involve teachers providing f... | [] | null | null | 2008.00524 | title_snapshot |
v155/manuelli21a | Keypoints into the Future: Self-Supervised Correspondence in Model-Based Reinforcement Learning | https://proceedings.mlr.press/v155/manuelli21a.html | [
"Lucas Manuelli",
"Yunzhu Li",
"Pete Florence",
"Russ Tedrake"
] | null | null | Predictive models have been at the core of many robotic systems, from quadrotors to walking robots. However, it has been challenging to develop and apply such models to practical robotic manipulation due to high-dimensional sensory observations such as images. Previous approaches to learning models in the context of ro... | [] | null | null | 2009.05085 | title_snapshot |
v155/wang21d | Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous | https://proceedings.mlr.press/v155/wang21d.html | [
"Rose Wang",
"J. Chase Kew",
"Dennis Lee",
"Tsang-Wei Lee",
"Tingnan Zhang",
"Brian Ichter",
"Jie Tan",
"Aleksandra Faust"
] | null | null | Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decent... | [] | null | null | 2003.06906 | title_snapshot |
v155/zeng21a | Transporter Networks: Rearranging the Visual World for Robotic Manipulation | https://proceedings.mlr.press/v155/zeng21a.html | [
"Andy Zeng",
"Pete Florence",
"Jonathan Tompson",
"Stefan Welker",
"Jonathan Chien",
"Maria Attarian",
"Travis Armstrong",
"Ivan Krasin",
"Dan Duong",
"Vikas Sindhwani",
"Johnny Lee"
] | null | null | Robotic manipulation can be formulated as inducing a sequence of spatial displacements: where the space being moved can encompass an object, part of an object, or end effector. In this work, we propose the Transporter Network, a simple model architecture that rearranges deep features to infer spatial displacements from... | [] | null | null | 2010.14406 | title_snapshot |
v155/reddy21a | Assisted Perception: Optimizing Observations to Communicate State | https://proceedings.mlr.press/v155/reddy21a.html | [
"Siddharth Reddy",
"Sergey Levine",
"Anca Dragan"
] | null | null | We aim to help users estimate the state of the world in tasks like robotic teleoperation and navigation with visual impairment, where user may have systematic biases that lead to suboptimal behavior: they might struggle to process observations from multiple sensors simultaneously, receive delayed observations, or under... | [] | null | null | 2008.02840 | title_snapshot |
v155/shaj21a | Action-Conditional Recurrent Kalman Networks For Forward and Inverse Dynamics Learning | https://proceedings.mlr.press/v155/shaj21a.html | [
"Vaisakh Shaj",
"Philipp Becker",
"Dieter Büchler",
"Harit Pandya",
"Niels van Duijkeren",
"C. James Taylor",
"Marc Hanheide",
"Gerhard Neumann"
] | null | null | Estimating accurate forward and inverse dynamics models is a crucial component of model-based control for sophisticated robots such as robots driven by hydraulics, artificial muscles, or robots dealing with different contact situations. Analytic models to such processes are often unavailable or inaccurate due to comple... | [] | null | null | 2010.10201 | title_snapshot |
v155/grannen21a | Untangling Dense Knots by Learning Task-Relevant Keypoints | https://proceedings.mlr.press/v155/grannen21a.html | [
"Jennifer Grannen",
"Priya Sundaresan",
"Brijen Thananjeyan",
"Jeffrey Ichnowski",
"Ashwin Balakrishna",
"Vainavi Viswanath",
"Michael Laskey",
"Joseph Gonzalez",
"Ken Goldberg"
] | null | null | Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We consider dense (tight) knots that lack space between self-intersections and present an iterative approach that uses learned geometric struct... | [] | null | null | 2011.04999 | title_snapshot |
v155/chow21a | Safe Policy Learning for Continuous Control | https://proceedings.mlr.press/v155/chow21a.html | [
"Yinlam Chow",
"Ofir Nachum",
"Aleksandra Faust",
"Edgar Dueñez-Guzman",
"Mohammad Ghavamzadeh"
] | null | null | We study continuous action reinforcement learning problems in which it is crucial that the agent interacts with the environment only through near-safe policies, i.e., policies that keep the agent in desirable situations, both during training and at convergence. We formulate these problems as {\em constrained} Markov de... | [] | null | null | null | null |
v155/sharma21a | Learning to Compose Hierarchical Object-Centric Controllers for Robotic Manipulation | https://proceedings.mlr.press/v155/sharma21a.html | [
"Mohit Sharma",
"Jacky Liang",
"Jialiang Zhao",
"Alex Lagrassa",
"Oliver Kroemer"
] | null | null | Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining contact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers... | [] | null | null | 2011.04627 | title_snapshot |
v155/sharma21b | Relational Learning for Skill Preconditions | https://proceedings.mlr.press/v155/sharma21b.html | [
"Mohit Sharma",
"Oliver Kroemer"
] | null | null | To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill. As robots begin to operate in dynamic and unstructured environments, these precondition models will need to generalize to variable number of objects with different shapes and sizes. In this work, we... | [] | null | null | 2012.01693 | title_snapshot |
v155/brito21a | Social-VRNN: One-Shot Multi-modal Trajectory Prediction for Interacting Pedestrians | https://proceedings.mlr.press/v155/brito21a.html | [
"Bruno Ferreira de Brito",
"Hai Zhu",
"Wei Pan",
"Javier Alonso-Mora"
] | null | null | Prediction of human motions is key for safe navigation of autonomous robots among humans. In cluttered environments, several motion hypotheses may exist for a pedestrian, due to its interactions with the environment and other pedestrians. Previous works for estimating multiple motion hypotheses require a large number o... | [] | null | null | 2010.09056 | title_snapshot |
v155/xie21b | MuGNet: Multi-Resolution Graph Neural Network for Segmenting Large-Scale Pointclouds | https://proceedings.mlr.press/v155/xie21b.html | [
"Liuyue Xie",
"Tomotake Furuhata",
"Kenji Shimada"
] | null | null | In this paper, we propose a multi-resolution deep-learning architecture to segment dense large-scale pointclouds semantically. Dense pointcloud data require a computationally expensive feature encoding process before semantic segmentation. Previous work has used different approaches to drastically downsample from the o... | [] | null | null | null | null |
v155/da21a | Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion | https://proceedings.mlr.press/v155/da21a.html | [
"Xingye Da",
"Zhaoming Xie",
"David Hoeller",
"Byron Boots",
"Anima Anandkumar",
"Yuke Zhu",
"Buck Babich",
"Animesh Garg"
] | null | null | We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago). The system consists of a high-level controller that learns to choose from a set of primitives in response to changes in the environment and a low-... | [] | null | null | 2009.10019 | title_snapshot |
v155/zhao21b | TNT: Target-driven Trajectory Prediction | https://proceedings.mlr.press/v155/zhao21b.html | [
"Hang Zhao",
"Jiyang Gao",
"Tian Lan",
"Chen Sun",
"Ben Sapp",
"Balakrishnan Varadarajan",
"Yue Shen",
"Yi Shen",
"Yuning Chai",
"Cordelia Schmid",
"Congcong Li",
"Dragomir Anguelov"
] | null | null | Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and intrinsically multimodal. Our key insight is that for prediction within a moderate time horizon, the future modes can be effectively captur... | [] | null | null | 2008.08294 | title_snapshot |
v155/han21a | Planning Paths Through Unknown Space by Imagining What Lies Therein | https://proceedings.mlr.press/v155/han21a.html | [
"Yutao Han",
"Jacopo Banfi",
"Mark Campbell"
] | null | null | This paper presents a novel framework for planning paths in maps containing unknown spaces, such as from occlusions. Our approach takes as input a semantically-annotated point cloud, and leverages an image inpainting neural network to generate a reasonable model of unknown space as free or occupied. Our validation camp... | [] | null | null | 2011.07316 | title_snapshot |
v155/jeong21a | Learning Dexterous Manipulation from Suboptimal Experts | https://proceedings.mlr.press/v155/jeong21a.html | [
"Rae Jeong",
"Jost Tobias Springenberg",
"Jackie Kay",
"Dan Zheng",
"Alexandre Galashov",
"Nicolas Heess",
"Francesco Nori"
] | null | null | Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail ... | [] | null | null | 2010.08587 | title_snapshot |
v155/matl21a | STReSSD: Sim-To-Real from Sound for Stochastic Dynamics | https://proceedings.mlr.press/v155/matl21a.html | [
"Carolyn Matl",
"Yashraj Narang",
"Dieter Fox",
"Ruzena Bajcsy",
"Fabio Ramos"
] | null | null | Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stochastic dynamics, demonstrated for the canonical case of a bouncing ball. A physically-motivated noise model is presented to capture stochasti... | [] | null | null | 2011.03136 | title_snapshot |
v155/ma21a | Contrastive Variational Reinforcement Learning for Complex Observations | https://proceedings.mlr.press/v155/ma21a.html | [
"Xiao Ma",
"SIWEI CHEN",
"David Hsu",
"Wee Sun Lee"
] | null | null | Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environments remains a major challenge. This paper presents Contrastive Variational Reinforcement Learning (CVRL), a model-based method that tackles c... | [] | null | null | 2008.02430 | title_snapshot |
v155/segal21a | Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs | https://proceedings.mlr.press/v155/segal21a.html | [
"Sean Segal",
"Eric Kee",
"Wenjie Luo",
"Abbas Sadat",
"Ersin Yumer",
"Raquel Urtasun"
] | null | null | In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling efficient tagging of many attributes and faster learning of new attributes with limited data. Importantly, the embedding is spatio-temporally awar... | [] | null | null | 2011.06165 | title_snapshot |
v155/joshi21a | Asynchronous Deep Model Reference Adaptive Control | https://proceedings.mlr.press/v155/joshi21a.html | [
"Girish Joshi",
"Jasvir Virdi",
"Girish Chowdhary"
] | null | null | In this paper, we present Asynchronous implementation of Deep Neural Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new neuro-adaptive control architecture through flight tests on a small quadcopter. We demonstrate that a single DMRAC controller can handle significant nonlinearities due to sev... | [] | null | null | 2011.02920 | title_snapshot |
v155/veer21a | Probably Approximately Correct Vision-Based Planning using Motion Primitives | https://proceedings.mlr.press/v155/veer21a.html | [
"Sushant Veer",
"Anirudha Majumdar"
] | null | null | This paper presents an approach for learning vision-based planners that provably generalize to novel environments (i.e., environments unseen during training). We leverage the Probably Approximately Correct (PAC)-Bayes framework to obtain an upper bound on the expected cost of policies across all environments. Minimizin... | [] | null | null | 2002.12852 | title_snapshot |
v155/villalonga21a | Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering | https://proceedings.mlr.press/v155/villalonga21a.html | [
"Maria Bauza Villalonga",
"Alberto Rodriguez",
"Bryan Lim",
"Eric Valls",
"Theo Sechopoulos"
] | null | null | In this paper, we present an approach to tactile pose estimation from the first touch for known objects. First, we create an object-agnostic map from real tactile observations to contact shapes. Next, for a new object with known geometry, we learn a tailored perception model completely in simulation. To do so, we simul... | [] | null | null | 2012.05205 | title_snapshot |
v155/wang21e | ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning | https://proceedings.mlr.press/v155/wang21e.html | [
"Yufei Wang",
"Narasimhan Gautham",
"Xingyu Lin",
"Brian Okorn",
"David Held"
] | null | null | Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning ... | [] | null | null | 2011.06777 | title_snapshot |
v155/pinneri21a | Sample-efficient Cross-Entropy Method for Real-time Planning | https://proceedings.mlr.press/v155/pinneri21a.html | [
"Cristina Pinneri",
"Shambhuraj Sawant",
"Sebastian Blaes",
"Jan Achterhold",
"Joerg Stueckler",
"Michal Rolinek",
"Georg Martius"
] | null | null | Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse-reward environments. However, their sampling inefficiency prevents them from being used for real-time planning and control. We propose an ... | [] | null | null | 2008.06389 | title_snapshot |
v155/sandha21a | Sim2Real Transfer for Deep Reinforcement Learning with Stochastic State Transition Delays | https://proceedings.mlr.press/v155/sandha21a.html | [
"Sandeep Singh Sandha",
"Luis Garcia",
"Bharathan Balaji",
"Fatima Anwar",
"Mani Srivastava"
] | null | null | Deep Reinforcement Learning (RL) has demonstrated to be useful for a wide variety of robotics applications. To address sample efficiency and safety during training, it is common to train Deep RL policies in a simulator and then deploy to the real world, a process called Sim2Real transfer. For robotics applications, th... | [] | null | null | null | null |
v155/hafner21a | Towards General and Autonomous Learning of Core Skills: A Case Study in Locomotion | https://proceedings.mlr.press/v155/hafner21a.html | [
"Roland Hafner",
"Tim Hertweck",
"Philipp Kloeppner",
"Michael Bloesch",
"Michael Neunert",
"Markus Wulfmeier",
"Saran Tunyasuvunakool",
"Nicolas Heess",
"Martin Riedmiller"
] | null | null | Modern Reinforcement Learning (RL) algorithms promise to solve difficult motor control problems directly from raw sensory inputs. Their attraction is due in part to the fact that they can represent a general class of methods that allow to learn a solution with a reasonably set reward and minimal prior knowledge, even i... | [] | null | null | 2008.12228 | title_snapshot |
v155/rabiee21a | IV-SLAM: Introspective Vision for Simultaneous Localization and Mapping | https://proceedings.mlr.press/v155/rabiee21a.html | [
"Sadegh Rabiee",
"Joydeep Biswas"
] | null | null | Existing solutions to visual simultaneous localization and mapping (V-SLAM) assume that errors in feature extraction and matching are independent and identically distributed (i.i.d), but this assumption is known to not be true – features extracted from low-contrast regions of images exhibit wider error distributions th... | [] | null | null | 2008.02760 | title_snapshot |
v155/ha21c | Learning to Walk in the Real World with Minimal Human Effort | https://proceedings.mlr.press/v155/ha21c.html | [
"Sehoon Ha",
"Peng Xu",
"Zhenyu Tan",
"Sergey Levine",
"Jie Tan"
] | null | null | Reliable and stable locomotion has been one of the most fundamental challenges for legged robots. Deep reinforcement learning (deep RL) has emerged as a promising method for developing such control policies autonomously. In this paper, we develop a system for learning legged locomotion policies with deep RL in the real... | [] | null | null | 2002.08550 | title_snapshot |
v155/cruz21a | Generation of Realistic Images for Learning in Simulation using FeatureGAN | https://proceedings.mlr.press/v155/cruz21a.html | [
"Nicolas Cruz",
"Javier Ruiz-del-Solar"
] | null | null | This paper presents FeatureGan, a methodology to train image translators (generators) using an unpaired image training set. FeatureGan is based on the use of Generative Adversarial Networks (GAN) and has three main novel components: (i) the use of a feature loss to ensure alignment between the input and the generated i... | [] | null | null | null | null |
v155/schiel21a | Incremental learning of EMG-based control commands using Gaussian Processes | https://proceedings.mlr.press/v155/schiel21a.html | [
"Felix Schiel",
"Annette Hagengruber",
"Jörn Vogel",
"Rudolph Triebel"
] | null | null | Myoelectric control is the process of controlling a prosthesis or an assistive robot by using electrical signals of the muscles. Pattern recognition in myoelectric control is a challenging field, since the underlying distribution of the signal is likely to change during the application. Covariate shifts, including chan... | [] | null | null | null | null |
v155/song21a | Flightmare: A Flexible Quadrotor Simulator | https://proceedings.mlr.press/v155/song21a.html | [
"Yunlong Song",
"Selim Naji",
"Elia Kaufmann",
"Antonio Loquercio",
"Davide Scaramuzza"
] | null | null | State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic. In this work, we propose a paradigm shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users. We ... | [] | null | null | 2009.00563 | title_snapshot |
v155/chen21a | Hardware as Policy: Mechanical and Computational Co-Optimization using Deep Reinforcement Learning | https://proceedings.mlr.press/v155/chen21a.html | [
"Tianjian Chen",
"Zhanpeng He",
"Matei Ciocarlie"
] | null | null | Deep Reinforcement Learning (RL) has shown great success in learning complex control policies for a variety of applications in robotics. However, in most such cases, the hardware of the robot has been considered immutable, modeled as part of the environment. In this study, we explore the problem of learning hardware a... | [] | null | null | 2008.04460 | title_snapshot |
v155/frossard21a | StrObe: Streaming Object Detection from LiDAR Packets | https://proceedings.mlr.press/v155/frossard21a.html | [
"Davi Frossard",
"Shun Da Suo",
"Sergio Casas",
"James Tu",
"Raquel Urtasun"
] | null | null | Many modern robotics systems employ LiDAR as their main sensing modality due to its geometrical richness. Rolling shutter LIDARs are particularly common, in which a sweep is built through the accumulation of points over an entire revolution of the sensor, thus producing a 360 point cloud of the scene. Modern perception... | [] | null | null | 2011.06425 | title_snapshot |
v155/zhao21c | Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps | https://proceedings.mlr.press/v155/zhao21c.html | [
"Jialiang Zhao",
"Daniel Troniak",
"Oliver Kroemer"
] | null | null | Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks. Knowledge of the objects’ geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute. However, several factors contribute to the challenges of realizing these grasps such ... | [] | null | null | 2011.02462 | title_snapshot |
v155/vadivelu21a | Learning to Communicate and Correct Pose Errors | https://proceedings.mlr.press/v155/vadivelu21a.html | [
"Nicholas Vadivelu",
"Mengye Ren",
"James Tu",
"Jingkang Wang",
"Raquel Urtasun"
] | null | null | Learned communication makes multi-agent systems more effective by aggregating distributed information. However, it also exposes individual agents to the threat of erroneous messages they receive. In this paper, we study the setting proposed in V2VNet, where nearby self-driving vehicles jointly perform object detection ... | [] | null | null | 2011.05289 | title_snapshot |
v155/haris21a | Visual Localization and Mapping with Hybrid SFA | https://proceedings.mlr.press/v155/haris21a.html | [
"Muhammad Haris",
"Mathias Franzius",
"Ute Bauer-Wersing",
"Sai Krishna Kaushik Karanam"
] | null | null | Visual localization is a crucial requirement in mobile robotics, field and service robotics, and self-driving cars. Recently, unsupervised learning with Slow Feature Analysis (SFA) has shown to produce spatial representations that enable localization from holistic images. The approach is faster and much less complex th... | [] | null | null | null | null |
v155/murooka21a | EXI-Net: EXplicitly/Implicitly Conditioned Network for Multiple Environment Sim-to-Real Transfer | https://proceedings.mlr.press/v155/murooka21a.html | [
"Takayuki Murooka",
"Masashi Hamaya",
"Felix von Drigalski",
"Kazutoshi Tanaka",
"Yoshihisa Ijiri"
] | null | null | Sim-to-real transfer is attractive for robot learning, as it avoids the high cost of collecting data with real robots, but transferring agents from simulation to the real world is challenging. Previous studies have presented promising methods to solve this problem, but they may fail when a wider range of dynamics has t... | [] | null | null | null | null |
v155/boerdijk21a | Self-Supervised Object-in-Gripper Segmentation from Robotic Motions | https://proceedings.mlr.press/v155/boerdijk21a.html | [
"Wout Boerdijk",
"Martin Sundermeyer",
"Maximilian Durner",
"Rudolph Triebel"
] | null | null | Accurate object segmentation is a crucial task in the context of robotic manipulation. However, creating sufficient annotated training data for neural networks is particularly time consuming and often requires manual labeling. To this end, we propose a simple, yet robust solution for learning to segment unknown objects... | [] | null | null | 2002.04487 | title_snapshot |
v155/zhao21d | MELD: Meta-Reinforcement Learning from Images via Latent State Models | https://proceedings.mlr.press/v155/zhao21d.html | [
"Zihao Zhao",
"Anusha Nagabandi",
"Kate Rakelly",
"Chelsea Finn",
"Sergey Levine"
] | null | null | Meta-reinforcement learning algorithms can enable autonomous agents, such as robots, to quickly acquire new behaviors by leveraging prior experience in a set of related training tasks. However, the onerous data requirements of meta-training compounded with the challenge of learning from sensory inputs such as images ha... | [] | null | null | 2010.13957 | title_snapshot |
v155/chen21b | Learning from Suboptimal Demonstration via Self-Supervised Reward Regression | https://proceedings.mlr.press/v155/chen21b.html | [
"Letian Chen",
"Rohan Paleja",
"Matthew Gombolay"
] | null | null | Learning from Demonstration (LfD) seeks to democratize robotics by enabling non-roboticist end-users to teach robots to perform a task by providing a human demonstration. However, modern LfD techniques, e.g. inverse reinforcement learning (IRL), assume users provide at least stochastically optimal demonstrations. This ... | [] | null | null | 2010.11723 | title_snapshot |
v155/lambert21a | Stein Variational Model Predictive Control | https://proceedings.mlr.press/v155/lambert21a.html | [
"Alexander Lambert",
"Fabio Ramos",
"Byron Boots",
"Dieter Fox",
"Adam Fishman"
] | null | null | Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but remain limited when dealing with complex probability distributions. In this paper, we propose a generalization of MPC that represents a multitu... | [] | null | null | 2011.07641 | title_snapshot |
v155/franzese21a | Learning Interactively to Resolve Ambiguity in Reference Frame Selection | https://proceedings.mlr.press/v155/franzese21a.html | [
"Giovanni Franzese",
"Carlos Celemin",
"Jens Kober"
] | null | null | In Learning from Demonstrations, ambiguities can lead to bad generalization of the learned policy. This paper proposes a framework called Learning Interactively to Resolve Ambiguity (LIRA), that recognizes ambiguous situations, in which more than one action have similar probabilities, avoids a random action selection, ... | [] | null | null | null | null |
v155/chen21c | Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning | https://proceedings.mlr.press/v155/chen21c.html | [
"Le Chen",
"Yunke Ao",
"Florian Tschopp",
"Andrei Cramariuc",
"Michel Breyer",
"Jen Jen Chung",
"Roland Siegwart",
"Cesar Cadena"
] | null | null | Visual-inertial systems rely on precise calibrations of both camera intrinsics and inter-sensor extrinsics, which typically require manually performing complex motions in front of a calibration target. In this work we present a novel approach to obtain favorable trajectories for visual-inertial system calibration, usin... | [] | null | null | 2011.02574 | title_snapshot |
v155/mehta21a | A User’s Guide to Calibrating Robotic Simulators | https://proceedings.mlr.press/v155/mehta21a.html | [
"Bhairav Mehta",
"Ankur Handa",
"Dieter Fox",
"Fabio Ramos"
] | null | null | Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first, before deployed to real world systems, saving on time and costs. Despite significant progress on the development of sim-to-real algorithms, the analysis of different m... | [] | null | null | 2011.08985 | title_judge |
v155/boffi21a | Learning Stability Certificates from Data | https://proceedings.mlr.press/v155/boffi21a.html | [
"Nicholas Boffi",
"Stephen Tu",
"Nikolai Matni",
"Jean-Jacques Slotine",
"Vikas Sindhwani"
] | null | null | Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function which guarantees a desired property. However, algorithms for synthesizing certificate functions typically require a closed-form analytical expression ... | [] | null | null | 2008.05952 | title_snapshot |
v155/lindemann21a | Learning Hybrid Control Barrier Functions from Data | https://proceedings.mlr.press/v155/lindemann21a.html | [
"Lars Lindemann",
"Haimin Hu",
"Alexander Robey",
"Hanwen Zhang",
"Dimos Dimarogonas",
"Stephen Tu",
"Nikolai Matni"
] | null | null | Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from data. In particular, we assume a setting in which the system dynamics are known and in which data exhibiting safe system behavior is availa... | [] | null | null | 2011.04112 | title_snapshot |
v155/zhang21a | Map-Adaptive Goal-Based Trajectory Prediction | https://proceedings.mlr.press/v155/zhang21a.html | [
"Lingyao Zhang",
"Po-Hsun Su",
"Jerrick Hoang",
"Galen Clark Haynes",
"Micol Marchetti-Bowick"
] | null | null | We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths – which are generated at run time and therefore dynamically adapt to the s... | [] | null | null | 2009.04450 | title_snapshot |
v155/banerjee21a | The RobotSlang Benchmark: Dialog-guided Robot Localization and Navigation | https://proceedings.mlr.press/v155/banerjee21a.html | [
"Shurjo Banerjee",
"Jesse Thomason",
"Jason Corso"
] | null | null | Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural languag... | [] | null | null | 2010.12639 | title_snapshot |
v155/blumenkamp21a | The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning | https://proceedings.mlr.press/v155/blumenkamp21a.html | [
"Jan Blumenkamp",
"Amanda Prorok"
] | null | null | Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. These works use models of cooperative multi-agent systems whereby agents strive to a... | [] | null | null | 2008.02616 | title_snapshot |
v155/zambelli21a | Learning rich touch representations through cross-modal self-supervision | https://proceedings.mlr.press/v155/zambelli21a.html | [
"Martina Zambelli",
"Yusuf Aytar",
"Francesco Visin",
"Yuxiang Zhou",
"Raia Hadsell"
] | null | null | The sense of touch is fundamental in several manipulation tasks, but rarely used in robot manipulation. In this work we tackle the problem of learning rich touch features from cross-modal self-supervision. We evaluate them identifying objects and their properties in a few-shot classification setting. Two new datasets a... | [] | null | null | 2101.08616 | title_snapshot |
v155/ren21a | Generalization Guarantees for Imitation Learning | https://proceedings.mlr.press/v155/ren21a.html | [
"Allen Ren",
"Sushant Veer",
"Anirudha Majumdar"
] | null | null | Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert’s policies. In this paper, we present rigorous generalization guarantees for imitation learning by leveraging the P... | [] | null | null | 2008.01913 | title_snapshot |
v155/ji21a | Multi-Modal Anomaly Detection for Unstructured and Uncertain Environments | https://proceedings.mlr.press/v155/ji21a.html | [
"Tianchen Ji",
"Sri Theja Vuppala",
"Girish Chowdhary",
"Katherine Driggs-Campbell"
] | null | null | To achieve high-levels of autonomy, modern robots require the ability to detect and recover from anomalies and failures with minimal human supervision. Multi-modal sensor signals could provide more information for such anomaly detection tasks; however, the fusion of high-dimensional and heterogeneous sensor modalities ... | [] | null | null | 2012.08637 | title_snapshot |
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