Datasets:

ai-conferences
/

CoRL2021

paper_id string	title string	paper_url string	authors list	type string	primary_area string	abstract large_string	keywords list	TL;DR large_string	submission_number int64	arxiv_id string	arxiv_id_source string
sWBqOL5Nh4P	Learning Density Distribution of Reachable States for Autonomous Systems	https://openreview.net/forum?id=sWBqOL5Nh4P	[ "Yue Meng", "Dawei Sun", "Zeng Qiu", "Md Tawhid Bin Waez", "Chuchu Fan" ]	Poster	null	State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazardous situations. In this work, we propose a data-driven method to compute the density distribution of reachable states for nonlinear and eve...	[ "Reachability Density Distribution", "Learning Density Distribution", "Liouville Theorem" ]	null	1	2109.06728	title_snapshot
_daq0uh6yXr	Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks	https://openreview.net/forum?id=_daq0uh6yXr	[ "Bohan Wu", "Suraj Nair", "Li Fei-Fei", "Chelsea Finn" ]	Poster	null	In this paper, we study the problem of learning a repertoire of low-level skills from raw images that can be sequenced to complete long-horizon visuomotor tasks. Reinforcement learning (RL) is a promising approach for acquiring short-horizon skills autonomously. However, the focus of RL algorithms has largely been on t...	[ "model-based reinforcement learning", "long-horizon planning" ]	null	2	2109.10312	title_snapshot
ar8FMzaZbcq	Fully Self-Supervised Class Awareness in Dense Object Descriptors	https://openreview.net/forum?id=ar8FMzaZbcq	[ "Denis Hadjivelichkov", "Dimitrios Kanoulas" ]	Poster	null	We address the problem of inferring self-supervised dense semantic correspondences between objects in multi-object scenes. The method introduces learning of class-aware dense object descriptors by providing either unsupervised discrete labels or confidence in object similarities. We quantitatively and qualitatively sho...	[ "Self-Supervision", "Descriptor Learning", "Object Correspondence" ]	null	4	2110.01957	title_snapshot
tfLu5W6SW5J	Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation	https://openreview.net/forum?id=tfLu5W6SW5J	[ "Suraj Nair", "Eric Mitchell", "Kevin Chen", "brian ichter", "Silvio Savarese", "Chelsea Finn" ]	Poster	null	We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. In order to accomplish this, humans need easy and effective ways of specifying tasks to the robot. Goal images are one popular form of task specification, as they are already grounded in the ro...	[ "Natural Language", "Offline Reinforcement Learning", "Visuomotor Manipulation" ]	null	5	2109.01115	title_snapshot
eIk6eBz3Wlt	Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processes	https://openreview.net/forum?id=eIk6eBz3Wlt	[ "Jongseok Lee", "Jianxiang Feng", "Matthias Humt", "Marcus Gerhard Müller", "Rudolph Triebel" ]	Poster	null	This paper presents a probabilistic framework to obtain both reliable and fast uncertainty estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a practical and principled combination of DNNs with sparse Gaussian Processes (GPs). We prove theoretically that DNNs can be seen as a special c...	[ "Robotic Introspection", "Bayesian Deep Learning", "Gaussian Processes" ]	null	17	2109.09690	title_snapshot
ht3aHpc1hUt	Structure from Silence: Learning Scene Structure from Ambient Sound	https://openreview.net/forum?id=ht3aHpc1hUt	[ "Ziyang Chen", "Xixi Hu", "Andrew Owens" ]	Oral	null	From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired aud...	[ "audio perception", "multi-modal learning", "self-supervision", "navigation" ]	null	18	2111.05846	title_snapshot
d_SWJhyKfVw	Rapid Exploration for Open-World Navigation with Latent Goal Models	https://openreview.net/forum?id=d_SWJhyKfVw	[ "Dhruv Shah", "Benjamin Eysenbach", "Nicholas Rhinehart", "Sergey Levine" ]	Oral	null	We describe a robotic learning system for autonomous exploration and navigation in diverse, open-world environments. At the core of our method is a learned latent variable model of distances and actions, along with a non-parametric topological memory of images. We use an information bottleneck to regularize the learned...	[ "robot learning", "navigation", "exploration" ]	null	19	2104.05859	title_snapshot
tmZsuYPmQ9m	Anomaly Detection in Multi-Agent Trajectories for Automated Driving	https://openreview.net/forum?id=tmZsuYPmQ9m	[ "Julian Wiederer", "Arij Bouazizi", "Marco Troina", "Ulrich Kressel", "Vasileios Belagiannis" ]	Poster	null	Human drivers can recognise fast abnormal driving situations to avoid accidents. Similar to humans, automated vehicles are supposed to perform anomaly detection. In this work, we propose the spatio-temporal graph auto-encoder for learning normal driving behaviours. Our innovation is the ability to jointly learn multipl...	[ "Anomaly Detection", "Multi-Agent Trajectory", "Graph Neural Networks", "Automated Driving" ]	null	20	2110.07922	title_snapshot
L0tXWRrB9yw	SCAPE: Learning Stiffness Control from Augmented Position Control Experiences	https://openreview.net/forum?id=L0tXWRrB9yw	[ "Mincheol Kim", "Scott Niekum", "Ashish Deshpande" ]	Poster	null	We introduce a sample-efficient method for learning state-dependent stiffness control policies for dexterous manipulation. The ability to control stiffness facilitates safe and reliable manipulation by providing compliance and robustness to uncertainties. Most current reinforcement learning approaches to achieve roboti...	[ "Manipulation", "stiffness control", "reinforcement learning" ]	null	21	2102.08442	title_snapshot
MWtinPDqfZg	Learning Feasibility to Imitate Demonstrators with Different Dynamics	https://openreview.net/forum?id=MWtinPDqfZg	[ "Zhangjie Cao", "Yilun Hao", "Mengxi Li", "Dorsa Sadigh" ]	Poster	null	The goal of learning from demonstrations is to learn a policy for an agent (imitator) by mimicking the behavior in the demonstrations. Prior works on learning from demonstrations assume that the demonstrations are collected by a demonstrator that has the same dynamics as the imitator. However, in many real-world appli...	[ "Imitation Learning", "Learning from Agents with Different Dynamics" ]	null	24	2110.15142	title_snapshot
EougVeukEH9	O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning	https://openreview.net/forum?id=EougVeukEH9	[ "Kaichun Mo", "Yuzhe Qin", "Fanbo Xiang", "Hao Su", "Leonidas Guibas" ]	Poster	null	Contrary to the vast literature in modeling, perceiving, and understanding agent-object (e.g., human-object, hand-object, robot-object) interaction in computer vision and robotics, very few past works have studied the task of object-object interaction, which also plays an important role in robotic manipulation and plan...	[ "Object-object Affordance", "Vision for Robotics", "Large-scale Learning" ]	null	27	2106.15087	title_snapshot
zv3NYgRZ7Qo	3D Neural Scene Representations for Visuomotor Control	https://openreview.net/forum?id=zv3NYgRZ7Qo	[ "Yunzhu Li", "Shuang Li", "Vincent Sitzmann", "Pulkit Agrawal", "Antonio Torralba" ]	Oral	null	Humans have a strong intuitive understanding of the 3D environment around us. The mental model of the physics in our brain applies to objects of different materials and enables us to perform a wide range of manipulation tasks that are far beyond the reach of current robots. In this work, we desire to learn models for d...	[ "learning-based dynamics modeling", "3d-aware representation learning", "neural radiance field", "robotic manipulation" ]	null	28	2107.04004	title_snapshot
9aVCUv3nKBg	Adversarially Robust Imitation Learning	https://openreview.net/forum?id=9aVCUv3nKBg	[ "Jianren Wang", "Ziwen Zhuang", "Yuyang Wang", "Hang Zhao" ]	Poster	null	Modern imitation learning (IL) utilizes deep neural networks (DNNs) as function approximators to mimic the policy of the expert demonstrations. However, DNNs can be easily fooled by subtle noise added to the input, which is even non-detectable by humans. This makes the learned agent vulnerable to attacks, especially in...	[ "Imitation Learning", "Adversarial Learning" ]	null	30	null	null
Qdb1ODTQTnL	Learning to Regrasp by Learning to Place	https://openreview.net/forum?id=Qdb1ODTQTnL	[ "Shuo Cheng", "Kaichun Mo", "Lin Shao" ]	Poster	null	In this paper, we explore whether a robot can learn to regrasp a diverse set of objects to achieve various desired grasp poses. Regrasping is needed whenever a robot's current grasp pose fails to perform desired manipulation tasks. Endowing robots with such an ability has applications in many domains such as manufactur...	[ "Regrasping", "Deep Learning", "Robotic Manipulation" ]	null	32	2109.08817	title_snapshot
Pp0Co2vU28N	IMU-Assisted Learning of Single-View Rolling Shutter Correction	https://openreview.net/forum?id=Pp0Co2vU28N	[ "Jiawei Mo", "Md Jahidul Islam", "Junaed Sattar" ]	Poster	null	Rolling shutter distortion is highly undesirable for photography and computer vision algorithms (e.g., visual SLAM) because pixels can be potentially captured at different times and poses. In this paper, we propose a deep neural network to predict depth and row-wise pose from a single image for rolling shutter correcti...	[ "Rolling Shutter Correction", "IMU", "Learning" ]	null	39	2011.03106	title_snapshot
0QJeE5hkyFZ	FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding	https://openreview.net/forum?id=0QJeE5hkyFZ	[ "Huy Ha", "Shuran Song" ]	Oral	null	High-velocity dynamic actions (e.g., fling or throw) play a crucial role in our everyday interaction with deformable objects by improving our efficiency and effectively expanding our physical reach range. Yet, most prior works have tackled cloth manipulation using exclusively single-arm quasi-static actions, which requ...	[ "Dynamic manipulation", "Cloth manipulation", "Self-supervised learning" ]	null	45	2105.03655	title_snapshot
9uFiX_HRsIL	CLIPort: What and Where Pathways for Robotic Manipulation	https://openreview.net/forum?id=9uFiX_HRsIL	[ "Mohit Shridhar", "Lucas Manuelli", "Dieter Fox" ]	Poster	null	How can we imbue robots with the ability to manipulate objects precisely but also to reason about them in terms of abstract concepts? Recent works in manipulation have shown that end-to-end networks can learn dexterous skills that require precise spatial reasoning, but these methods often fail to generalize to new goal...	[ "Manipulation", "Pre-trained Models", "Vision Language Grounding", "CLIP" ]	null	46	2109.12098	title_snapshot
2NcPgLa7yqD	Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image Translation	https://openreview.net/forum?id=2NcPgLa7yqD	[ "Alex Church", "John Lloyd", "raia hadsell", "Nathan F. Lepora" ]	Poster	null	Simulation has recently become key for deep reinforcement learning to safely and efficiently acquire general and complex control policies from visual and proprioceptive inputs. Tactile information is not usually considered despite its direct relation to environment interaction. In this work, we present a suite of simul...	[ "Tactile Robotics", "Sim2Real", "Reinforcement Learning" ]	null	47	2106.08796	title_snapshot
p-TBwVowXRH	Learning Multi-Stage Tasks with One Demonstration via Self-Replay	https://openreview.net/forum?id=p-TBwVowXRH	[ "Norman Di Palo", "Edward Johns" ]	Poster	null	In this work, we introduce a novel method to learn everyday-like multi-stage tasks from a single human demonstration, without requiring any prior object knowledge. Inspired by the recent Coarse-to-Fine Imitation Learning, we model imitation learning as a learned object reaching phase followed by an open-loop replay of ...	[ "Imitation Learning", "Multi-Stage Imitation Learning", "Robot Manipulation" ]	null	51	2111.07447	title_snapshot
TSuSGVkjuXd	Smooth Exploration for Robotic Reinforcement Learning	https://openreview.net/forum?id=TSuSGVkjuXd	[ "Antonin Raffin", "Jens Kober", "Freek Stulp" ]	Poster	null	Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploratio...	[ "Robotics", "Reinforcement Learning", "Exploration", "Real World" ]	null	53	2005.05719	title_snapshot
X2KJq-S11BC	Self-Improving Semantic Perception for Indoor Localisation	https://openreview.net/forum?id=X2KJq-S11BC	[ "Hermann Blum", "Francesco Milano", "René Zurbrügg", "Roland Siegwart", "Cesar Cadena", "Abel Gawel" ]	Poster	null	We propose a novel robotic system that can improve its perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. B...	[ "continual learning", "self-supervised learning", "online learning" ]	null	55	2105.01595	title_snapshot
DgCWxJyERoQ	The Boombox: Visual Reconstruction from Acoustic Vibrations	https://openreview.net/forum?id=DgCWxJyERoQ	[ "Boyuan Chen", "Mia Chiquier", "Hod Lipson", "Carl Vondrick" ]	Poster	null	Interacting with bins and containers is a fundamental task in robotics, making state estimation of the objects inside the bin critical. While robots often use cameras for state estimation, the visual modality is not always ideal due to occlusions and poor illumination. We introduce The Boombox, a container that uses s...	[ "Multimodal Perception", "Object State Estimation", "Audio" ]	null	60	2105.08052	title_snapshot
yhy25u-DrjR	Broadly-Exploring, Local-Policy Trees for Long-Horizon Task Planning	https://openreview.net/forum?id=yhy25u-DrjR	[ "brian ichter", "Pierre Sermanet", "Corey Lynch" ]	Poster	null	Long-horizon planning in realistic environments requires the ability to reason over sequential tasks in high-dimensional state spaces with complex dynamics. Classical motion planning algorithms, such as rapidly-exploring random trees, are capable of efficiently exploring large state spaces and computing long-horizon, s...	[ "RRT", "Task and Motion Planning", "Model-based Planning", "Tree-search" ]	null	61	2010.06491	title_snapshot
GhMZNcr54zt	Learning to Predict Vehicle Trajectories with Model-based Planning	https://openreview.net/forum?id=GhMZNcr54zt	[ "Haoran Song", "Di Luan", "Wenchao Ding", "Michael Y Wang", "Qifeng Chen" ]	Poster	null	Predicting the future trajectories of on-road vehicles is critical for autonomous driving. In this paper, we introduce a novel prediction framework called PRIME, which stands for Prediction with Model-based Planning. Unlike recent prediction works that utilize neural networks to model scene context and produce unconstr...	[ "Trajectory Prediction", "Autonomous Driving" ]	null	64	2103.04027	title_snapshot
kgoWLlA33-U	LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks	https://openreview.net/forum?id=kgoWLlA33-U	[ "Albert Wilcox", "Ashwin Balakrishna", "Brijen Thananjeyan", "Joseph E. Gonzalez", "Ken Goldberg" ]	Poster	null	Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require extensive environment interaction when exploration is unconstrained. A promising strategy for learning in dynamically uncertain environments is r...	[ "Reinforcement Learning", "Imitation Learning", "Safety" ]	null	69	2107.04775	title_snapshot
KKBfrCzCVOn	ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning	https://openreview.net/forum?id=KKBfrCzCVOn	[ "Ryan Hoque", "Ashwin Balakrishna", "Ellen Novoseller", "Albert Wilcox", "Daniel S. Brown", "Ken Goldberg" ]	Oral	null	Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible to control the timing and length of interventions to both facilitate learning and limit burden on the human supervi...	[ "Imitation Learning", "Fleet Learning", "Human Robot Interaction" ]	null	70	2109.08273	title_snapshot
bEito8UUUmf	Probabilistic and Geometric Depth: Detecting Objects in Perspective	https://openreview.net/forum?id=bEito8UUUmf	[ "Tai Wang", "Xinge ZHU", "Jiangmiao Pang", "Dahua Lin" ]	Poster	null	3D object detection is an important capability needed in various practical applications such as driver assistance systems. Monocular 3D detection, a representative general setting among image-based approaches, provides a more economical solution than conventional settings relying on LiDARs but still yields unsatisfacto...	[ "Probabilistic and Geometric Depth", "Monocular 3D Detection" ]	null	71	2107.14160	title_snapshot
Ei3MOY2rDHB	My House, My Rules: Learning Tidying Preferences with Graph Neural Networks	https://openreview.net/forum?id=Ei3MOY2rDHB	[ "Ivan Kapelyukh", "Edward Johns" ]	Poster	null	Robots that arrange household objects should do so according to the user's preferences, which are inherently subjective and difficult to model. We present NeatNet: a novel Variational Autoencoder architecture using Graph Neural Network layers, which can extract a low-dimensional latent preference vector from a user by ...	[ "graph neural networks", "preference learning", "rearrangement tasks" ]	null	73	2111.03112	title_snapshot
0CE82_hBPzA	Learning Eye-in-Hand Camera Calibration from a Single Image	https://openreview.net/forum?id=0CE82_hBPzA	[ "Eugene Valassakis", "Kamil Dreczkowski", "Edward Johns" ]	Poster	null	Eye-in-hand camera calibration is a fundamental and long-studied problem in robotics. We present a study on using learning-based methods for solving this problem online from a single RGB image, whilst training our models with entirely synthetic data. We study three main approaches: one direct regression model that dire...	[ "Camera Calibration", "Robot Manipulation", "Sim-to-Real" ]	null	77	2111.01245	title_snapshot
ibktAcINCaj	Urban Driver: Learning to Drive from Real-world Demonstrations Using Policy Gradients	https://openreview.net/forum?id=ibktAcINCaj	[ "Oliver Scheel", "Luca Bergamini", "Maciej Wolczyk", "Błażej Osiński", "Peter Ondruska" ]	Poster	null	In this work we are the first to present an offline policy gradient method for learning imitative policies for complex urban driving from a large corpus of real-world demonstrations. This is achieved by building a differentiable data-driven simulator on top of perception outputs and high-fidelity HD maps of the area. I...	[ "Self-driving", "Learning from Demonstrations", "Simulation" ]	null	78	2109.13333	title_snapshot
YwDvofEWlEx	Learning Behaviors through Physics-driven Latent Imagination	https://openreview.net/forum?id=YwDvofEWlEx	[ "Antoine Richard", "Stephanie ARAVECCHIA", "Matthieu Geist", "Cédric Pradalier" ]	Oral	null	Model-based reinforcement learning (MBRL) consists in learning a so-called world model, a representation of the environment through interactions with it, then use it to train an agent. This approach is particularly interesting in the con-text of field robotics, as it alleviates the need to train online, and reduces the...	[ "Model-Based Reinforcement Learning", "Field Robotics", "Latent Models" ]	null	81	null	null
FzMHiDesj0I	TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo	https://openreview.net/forum?id=FzMHiDesj0I	[ "Lukas Koestler", "Nan Yang", "Niclas Zeller", "Daniel Cremers" ]	Poster	null	In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth...	[ "SLAM", "Dense Mapping", "Multi-view Stereo", "Deep Learning" ]	null	84	2111.07418	title_snapshot
f7KaqYLO3iE	A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation	https://openreview.net/forum?id=f7KaqYLO3iE	[ "Bernardo Aceituno", "Alberto Rodriguez", "Shubham Tulsiani", "Abhinav Gupta", "Mustafa Mukadam" ]	Poster	null	Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills. However, reasoning over mechanics and dexterous interactions can make it challenging to scale visual learning for contact-rich manipulation. In this work, we focus on the problem of visual dexterous planar manipulatio...	[ "Manipulation", "Visual learning", "Differentiable optimization" ]	null	85	2111.05318	title_snapshot
n1hDe9iK6ms	Learning Visible Connectivity Dynamics for Cloth Smoothing	https://openreview.net/forum?id=n1hDe9iK6ms	[ "Xingyu Lin", "Yufei Wang", "Zixuan Huang", "David Held" ]	Poster	null	Robotic manipulation of cloth remains challenging due to the complex dynamics of cloth, lack of a low-dimensional state representation, and self-occlusions. In contrast to previous model-based approaches that learn a pixel-based dynamics model or a compressed latent vector dynamics, we propose to learn a particle-based...	[ "Deformable object manipulation", "dynamics model learning" ]	null	86	2105.10389	title_snapshot
TavPBk4Zs9m	BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments	https://openreview.net/forum?id=TavPBk4Zs9m	[ "Sanjana Srivastava", "Chengshu Li", "Michael Lingelbach", "Roberto Martín-Martín", "Fei Xia", "Kent Elliott Vainio", "Zheng Lian", "Cem Gokmen", "Shyamal Buch", "Karen Liu", "Silvio Savarese", "Hyowon Gweon", "Jiajun Wu", "Li Fei-Fei" ]	Poster	null	We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation. These activities are designed to be realistic, diverse and complex, aiming to reproduce the challenges that agents must face in the rea...	[ "Embodied AI", "Benchmarking", "Household activities" ]	null	87	2108.03332	title_snapshot
jOSWHddP1fZ	Goal-Auxiliary Actor-Critic for 6D Robotic Grasping with Point Clouds	https://openreview.net/forum?id=jOSWHddP1fZ	[ "Lirui Wang", "Yu Xiang", "Wei Yang", "Arsalan Mousavian", "Dieter Fox" ]	Poster	null	6D robotic grasping beyond top-down bin-picking scenarios is a challenging task. Previous solutions based on 6D grasp synthesis with robot motion planning usually operate in an open-loop setting, which are sensitive to grasp synthesis errors. In this work, we propose a new method for learning closed-loop control polici...	[ "6D Robotic Grasping", "Imitation Learning", "Reinforcement Learning" ]	null	90	2010.00824	title_snapshot
YFF73MRGUIH	LENS: Localization enhanced by NeRF synthesis	https://openreview.net/forum?id=YFF73MRGUIH	[ "Arthur Moreau", "Nathan Piasco", "Dzmitry Tsishkou", "Bogdan Stanciulescu", "Arnaud de La Fortelle" ]	Poster	null	Neural Radiance Fields (NeRF) have recently demonstrated photorealistic results for the task of novel view synthesis. In this paper, we propose to apply novel view synthesis to the robot relocalization problem: we demonstrate improvement of camera pose regression thanks to an additional synthetic dataset rendered by th...	[ "visual localization", "camera pose regression", "novel view synthesis" ]	null	92	2110.06558	title_snapshot
WIE9t_UwOpM	Enhancing Consistent Ground Maneuverability by Robot Adaptation to Complex Off-Road Terrains	https://openreview.net/forum?id=WIE9t_UwOpM	[ "Sriram Siva", "Maggie Wigness", "John Rogers", "Hao Zhang" ]	Oral	null	Terrain adaptation is a critical ability for a ground robot to effectively traverse unstructured off-road terrain in real-world field environments such as forests. However, the expected or planned maneuvering behaviors cannot always be accurately executed due to setbacks such as reduced tire pressure. This inconsistenc...	[ "Robot Learning", "Off-road Navigation", "Terrain Adaptation" ]	null	96	2101.00290	title_judge
kSnfpHJBJOt	DexVIP: Learning Dexterous Grasping with Human Hand Pose Priors from Video	https://openreview.net/forum?id=kSnfpHJBJOt	[ "Priyanka Mandikal", "Kristen Grauman" ]	Poster	null	Dexterous multi-fingered robotic hands have a formidable action space, yet their morphological similarity to the human hand holds immense potential to accelerate robot learning. We propose DexVIP, an approach to learn dexterous robotic grasping from human-object interactions present in in-the-wild YouTube videos. We do...	[ "dexterous manipulation", "learning from observations", "learning from demonstrations", "computer vision" ]	null	100	2202.00164	title_snapshot
2g_c8okBd_2	Hierarchically Integrated Models: Learning to Navigate from Heterogeneous Robots	https://openreview.net/forum?id=2g_c8okBd_2	[ "Katie Kang", "Gregory Kahn", "Sergey Levine" ]	Poster	null	Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation. However, gathering such datasets with a single robot can be prohibitively expensive. Collecting data with multiple different robotic platforms with possibly different d...	[ "deep reinforcement learning", "multi-robot learning", "mobile navigation" ]	null	102	2106.13280	title_snapshot
4u25M570Iji	Motion Forecasting with Unlikelihood Training in Continuous Space	https://openreview.net/forum?id=4u25M570Iji	[ "Deyao Zhu", "Mohamed Zahran", "Li Erran Li", "Mohamed Elhoseiny" ]	Oral	null	Motion forecasting is essential for making safe and intelligent decisions in robotic applications such as autonomous driving. Existing methods often formulate it as a sequence-to-sequence prediction problem, solved in an encoder-decoder framework with a maximum likelihood estimation objective. State-of-the-art models l...	[]	null	104	null	null
5P_3bRWiRsF	RoCUS: Robot Controller Understanding via Sampling	https://openreview.net/forum?id=5P_3bRWiRsF	[ "Yilun Zhou", "Serena Booth", "Nadia Figueroa", "Julie Shah" ]	Poster	null	As robots are deployed in complex situations, engineers and end users must develop a holistic understanding of their behaviors, capabilities, and limitations. Some behaviors are directly optimized by the objective function. They often include success rate, completion time or energy consumption. Other behaviors -- e.g.,...	[ "debugging and evaluation", "algorithmic transparency" ]	null	106	2012.13615	title_snapshot
vm8Hr9YJHZ1	Fast and Efficient Locomotion via Learned Gait Transitions	https://openreview.net/forum?id=vm8Hr9YJHZ1	[ "Yuxiang Yang", "Tingnan Zhang", "Erwin Coumans", "Jie Tan", "Byron Boots" ]	Oral	null	We focus on the problem of developing energy efficient controllers for quadrupedal robots. Animals can actively switch gaits at different speeds to lower their energy consumption. In this paper, we devise a hierarchical learning framework, in which distinctive locomotion gaits and natural gait transitions emerge automa...	[ "Legged Locomotion", "Hierarchical Control", "Reinforcement Learning" ]	null	108	2104.04644	title_snapshot
LGaHnyg81sQ	Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration	https://openreview.net/forum?id=LGaHnyg81sQ	[ "Chen Wang", "Claudia Pérez-D'Arpino", "Danfei Xu", "Li Fei-Fei", "Karen Liu", "Silvio Savarese" ]	Poster	null	We present a method for learning human-robot collaboration policy from human-human collaboration demonstrations. An effective robot assistant must learn to handle diverse human behaviors shown in the demonstrations and be robust when the humans adjust their strategies during online task execution. Our method co-optimiz...	[ "Learning for Human-Robot Collaboration", "Imitation Learning" ]	null	109	2108.06038	title_snapshot
sIVC-oZN1PQ	Visually-Grounded Library of Behaviors for Manipulating Diverse Objects across Diverse Configurations and Views	https://openreview.net/forum?id=sIVC-oZN1PQ	[ "Jingyun Yang", "Hsiao-Yu Tung", "Yunchu Zhang", "Gaurav Pathak", "Ashwini Pokle", "Christopher G Atkeson", "Katerina Fragkiadaki" ]	Poster	null	We propose a visually-grounded library of behaviors approach for learning to manipulate diverse objects across varying initial and goal configurations and camera placements. Our key innovation is to disentangle the standard image-to-action mapping into two separate modules that use different types of perceptual input: ...	[ "robot learning", "visual representation", "interactive perception" ]	null	111	null	null
m5k1XdK5nI2	Exploring Adversarial Robustness of Multi-sensor Perception Systems in Self Driving	https://openreview.net/forum?id=m5k1XdK5nI2	[ "James Tu", "Huichen Li", "Xinchen Yan", "Mengye Ren", "Yun Chen", "Ming Liang", "Eilyan Bitar", "Ersin Yumer", "Raquel Urtasun" ]	Poster	null	Modern self-driving perception systems have been shown to improve upon processing complementary inputs such as LiDAR with images. In isolation, 2D images have been found to be extremely vulnerable to adversarial attacks. Yet, there are limited studies on the adversarial robustness of multi-modal models that fuse LiDAR ...	[ "Adversarial", "Self-Driving", "Detection", "Multimodal" ]	null	113	2101.06784	title_snapshot
50523z0PALg	Using Physics Knowledge for Learning Rigid-body Forward Dynamics with Gaussian Process Force Priors	https://openreview.net/forum?id=50523z0PALg	[ "Lucas Rath", "Andreas René Geist", "Sebastian Trimpe" ]	Poster	null	If a robot's dynamics are difficult to model solely through analytical mechanics, it is an attractive option to directly learn it from data. Yet, solely data-driven approaches require considerable amounts of data for training and do not extrapolate well to unseen regions of the system's state space. In this work, we em...	[ "Machine Learning", "Robotics", "Analytical mechanics" ]	null	114	null	null
oqZrUx-PRqb	Look Before You Leap: Safe Model-Based Reinforcement Learning with Human Intervention	https://openreview.net/forum?id=oqZrUx-PRqb	[ "Yunkun Xu", "Zhenyu Liu", "Guifang Duan", "Jiangcheng Zhu", "Xiaolong Bai", "Jianrong Tan" ]	Poster	null	Safety has become one of the main challenges of applying deep reinforcement learning to real world systems. Currently, the incorporation of external knowledge such as human oversight is the only means to prevent the agent from visiting the catastrophic state. In this paper, we propose MBHI, a novel framework for safe m...	[ "Safety RL", "Model-based RL", "Model Predict Control" ]	null	117	2111.05819	title_snapshot
JSC4KMlENqF	Demonstration-Guided Reinforcement Learning with Learned Skills	https://openreview.net/forum?id=JSC4KMlENqF	[ "Karl Pertsch", "Youngwoon Lee", "Yue Wu", "Joseph J Lim" ]	Poster	null	Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations. Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demo...	[ "Reinforcement Learning", "Imitation Learning", "Skill-Based Transfer Learning" ]	null	121	2107.10253	title_snapshot
DCfV0wDvtoV	Generalised Task Planning with First-Order Function Approximation	https://openreview.net/forum?id=DCfV0wDvtoV	[ "Jun Hao Alvin Ng", "Ron Petrick" ]	Poster	null	Real world robotics often operates in uncertain and dynamic environments where generalisation over different scenarios is of practical interest. In the absence of a model, value-based reinforcement learning can be used to learn a goal-directed policy. Typically, the interaction between robots and the objects in the env...	[ "task planning", "relational reinforcement learning", "transfer learning" ]	null	127	null	null
8xC5NNej-l_	S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning in Robotics	https://openreview.net/forum?id=8xC5NNej-l_	[ "Samarth Sinha", "Ajay Mandlekar", "Animesh Garg" ]	Poster	null	Offline reinforcement learning proposes to learn policies from large collected datasets without interacting with the physical environment. These algorithms have made it possible to learn useful skills from data that can then be deployed in the environment in real-world settings where interactions may be costly or dang...	[ "Offline Reinforcement Learning", "Data Augmentation", "Self-Supervised Learning" ]	null	128	2103.06326	title_judge
JrsfBJtDFdI	What Matters in Learning from Offline Human Demonstrations for Robot Manipulation	https://openreview.net/forum?id=JrsfBJtDFdI	[ "Ajay Mandlekar", "Danfei Xu", "Josiah Wong", "Soroush Nasiriany", "Chen Wang", "Rohun Kulkarni", "Li Fei-Fei", "Silvio Savarese", "Yuke Zhu", "Roberto Martín-Martín" ]	Oral	null	Imitating human demonstrations is a promising approach to endow robots with various manipulation capabilities. While recent advances have been made in imitation learning and batch (offline) reinforcement learning, a lack of open-source human datasets and reproducible learning methods make assessing the state of the fie...	[ "Imitation Learning", "Offline Reinforcement Learning", "Robot Manipulation" ]	null	129	2108.03298	title_snapshot
ofioIEZvJRG	Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning	https://openreview.net/forum?id=ofioIEZvJRG	[ "Sumeet Batra", "Zhehui Huang", "Aleksei Petrenko", "Tushar Kumar", "Artem Molchanov", "Gaurav S. Sukhatme" ]	Poster	null	We demonstrate the possibility of learning drone swarm controllers that are zero-shot transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement learning. We train policies parameterized by neural networks that are capable of controlling individual drones in a swarm in a fully decentralized ma...	[ "Deep Reinforcement Learning", "Robot Learning", "Multi-Agent Systems", "Quadrotors" ]	null	134	2109.07735	title_snapshot
FCoh4OLZ1Gg	Embodied Semantic Scene Graph Generation	https://openreview.net/forum?id=FCoh4OLZ1Gg	[ "Xinghang Li", "Di Guo", "Huaping Liu", "Fuchun Sun" ]	Poster	null	Semantic scene graph provides an effective way for intelligent agents to better understand the environment and it has been extensively used in many robotic applications. Existing work mainly focuses on generating the scene graph from the sensory information collected from a pre-defined path, while the environment shoul...	[ "Semantic Scene Graph", "Embodied Exploration", "Learning for Visual Navigation" ]	null	136	null	null
AlJXhEI6J5W	Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemble	https://openreview.net/forum?id=AlJXhEI6J5W	[ "Seunghyun Lee", "Younggyo Seo", "Kimin Lee", "Pieter Abbeel", "Jinwoo Shin" ]	Poster	null	Recent advance in deep offline reinforcement learning (RL) has made it possible to train strong robotic agents from offline datasets. However, depending on the quality of the trained agents and the application being considered, it is often desirable to fine-tune such agents via further online interactions. In this pape...	[ "Deep Reinforcement Learning", "Offline RL", "Fine-tuning" ]	null	137	2107.00591	title_snapshot
1yj7yMTtna	Towards Real Robot Learning in the Wild: A Case Study in Bipedal Locomotion	https://openreview.net/forum?id=1yj7yMTtna	[ "Michael Bloesch", "Jan Humplik", "Viorica Patraucean", "Roland Hafner", "Tuomas Haarnoja", "Arunkumar Byravan", "Noah Yamamoto Siegel", "Saran Tunyasuvunakool", "Federico Casarini", "Nathan Batchelor", "Francesco Romano", "Stefano Saliceti", "Martin Riedmiller", "S. M. Ali Eslami", "Nic...	Poster	null	Algorithms for self-learning systems have made considerable progress in recent years, yet safety concerns and the need for additional instrumentation have so far largely limited learning experiments with real robots to well controlled lab settings. In this paper, we demonstrate how a small bipedal robot can autonomousl...	[ "Legged Locomotion", "Reinforcement Learning", "Vision" ]	null	148	null	null
YeJaZBXlhPX	A Constrained Multi-Objective Reinforcement Learning Framework	https://openreview.net/forum?id=YeJaZBXlhPX	[ "Sandy Huang", "Abbas Abdolmaleki", "Giulia Vezzani", "Philemon Brakel", "Daniel J Mankowitz", "Michael Neunert", "Steven Bohez", "Yuval Tassa", "Nicolas Heess", "Martin Riedmiller", "raia hadsell" ]	Poster	null	Many real-world problems, especially in robotics, require that reinforcement learning (RL) agents learn policies that not only maximize an environment reward, but also satisfy constraints. We propose a high-level framework for solving such problems, that treats the environment reward and costs as separate objectives, a...	[ "constrained RL", "multi-objective RL", "deep RL" ]	null	149	null	null
a7DcA81EV4t	GRAC: Self-Guided and Self-Regularized Actor-Critic	https://openreview.net/forum?id=a7DcA81EV4t	[ "Lin Shao", "Yifan You", "Mengyuan Yan", "Shenli Yuan", "Qingyun Sun", "Jeannette Bohg" ]	Poster	null	Deep reinforcement learning (DRL) algorithms have successfully been demonstrated on a range of challenging decision making and control tasks. One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function. However, target networ...	[ "Deep Reinforcement Learning", "Q-learning" ]	null	150	2009.08973	title_snapshot
zwo1-MdMl1P	Robot Reinforcement Learning on the Constraint Manifold	https://openreview.net/forum?id=zwo1-MdMl1P	[ "Puze Liu", "Davide Tateo", "Haitham Bou Ammar", "Jan Peters" ]	Oral	null	Reinforcement learning in robotics is extremely challenging due to many practical issues, including safety, mechanical constraints, and wear and tear. Typically, these issues are not considered in the machine learning literature. One crucial problem in applying reinforcement learning in the real world is Safe Explorati...	[ "Robot Learning", "Reinforcement Learning", "Constrained Markov Decision Process", "Safe Exploration" ]	null	153	null	null
xQ8rr3-zpiH	Just Label What You Need: Fine-Grained Active Selection for P&P through Partially Labeled Scenes	https://openreview.net/forum?id=xQ8rr3-zpiH	[ "Sean Segal", "Nishanth Kumar", "Sergio Casas", "Wenyuan Zeng", "Mengye Ren", "Jingkang Wang", "Raquel Urtasun" ]	Poster	null	Self-driving vehicles must perceive and predict the future positions of nearby actors to avoid collisions and drive safely. A deep learning module is often responsible for this task, requiring large-scale, high-quality training datasets. Due to high labeling costs, active learning approaches are an appealing solution t...	[ "Self-Driving", "Active Learning" ]	null	157	2104.03956	title_judge
1GNV9SW95eJ	Learning Off-Policy with Online Planning	https://openreview.net/forum?id=1GNV9SW95eJ	[ "Harshit Sikchi", "Wenxuan Zhou", "David Held" ]	Oral	null	Reinforcement learning (RL) in low-data and risk-sensitive domains requires performant and flexible deployment policies that can readily incorporate constraints during deployment. One such class of policies are the semi-parametric H-step lookahead policies, which select actions using trajectory optimization over a dyna...	[ "Reinforcement Learning", "Trajectory Optimization", "Safety" ]	null	158	2008.10066	title_snapshot
A9P78VBwYKM	ObjectFolder: A Dataset of Objects with Implicit Visual, Auditory, and Tactile Representations	https://openreview.net/forum?id=A9P78VBwYKM	[ "Ruohan Gao", "Yen-Yu Chang", "Shivani Mall", "Li Fei-Fei", "Jiajun Wu" ]	Poster	null	Multisensory object-centric perception, reasoning, and interaction have been a key research topic in recent years. However, the progress in these directions is limited by the small set of objects available---synthetic objects are not realistic enough and are mostly centered around geometry, while real object datasets s...	[ "object dataset", "multisensory learning", "implicit representations" ]	null	161	2109.07991	title_snapshot
p9Pe-l9MMEq	Scaling Up Multi-Task Robotic Reinforcement Learning	https://openreview.net/forum?id=p9Pe-l9MMEq	[ "Dmitry Kalashnikov", "Jake Varley", "Yevgen Chebotar", "Benjamin Swanson", "Rico Jonschkowski", "Chelsea Finn", "Sergey Levine", "Karol Hausman" ]	Poster	null	General-purpose robotic systems must master a large repertoire of diverse skills. While reinforcement learning provides a powerful framework for acquiring individual behaviors, the time needed to acquire each skill makes the prospect of a generalist robot trained with RL daunting. In this paper, we study how a large-sc...	[ "Multi-Task Reinforcement Learning", "Robot Learning" ]	null	165	2104.08212	manual
YrRoft_OeKp	Evaluations of the Gap between Supervised and Reinforcement Lifelong Learning on Robotic Manipulation Tasks	https://openreview.net/forum?id=YrRoft_OeKp	[ "Fan Yang", "Chao Yang", "Huaping Liu", "Fuchun Sun" ]	Poster	null	Overcoming catastrophic forgetting is of great importance for deep learning and robotics. Recent lifelong learning research has great advances in supervised learning. However, little work focuses on reinforcement learning(RL). We focus on evaluating the performances of state-of-the-art lifelong learning algorithms on r...	[ "Lifelong Learning", "Reinforcement Learning", "Manipulation" ]	null	166	null	null
vrU4d2yjjUn	Assisted Robust Reward Design	https://openreview.net/forum?id=vrU4d2yjjUn	[ "Jerry Zhi-Yang He", "Anca Dragan" ]	Poster	null	Real-world robotic tasks require complex reward functions. When we define the problem the robot needs to solve, we pretend that a designer specifies this complex reward exactly, and it is set in stone from then on. In practice, however, reward design is an iterative process: the designer chooses a reward, eventually en...	[ "Reward Design", "Safety", "Human-in-the-loop", "Active Learning" ]	null	168	2111.09884	title_snapshot
GIgsuWifgIi	You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL	https://openreview.net/forum?id=GIgsuWifgIi	[ "Wonjoon Goo", "Scott Niekum" ]	Poster	null	The goal of offline reinforcement learning (RL) is to find an optimal policy given prerecorded trajectories. Many current approaches customize existing off-policy RL algorithms, especially actor-critic algorithms in which policy evaluation and improvement are iterated. However, the convergence of such approaches is not...	[ "offline reinforcement learning", "conservative policy evaluation" ]	null	169	2110.02304	title_snapshot
U0Q8CrtBJxJ	Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes	https://openreview.net/forum?id=U0Q8CrtBJxJ	[ "Alex X. Lee", "Coline Manon Devin", "Yuxiang Zhou", "Thomas Lampe", "Konstantinos Bousmalis", "Jost Tobias Springenberg", "Arunkumar Byravan", "Abbas Abdolmaleki", "Nimrod Gileadi", "David Khosid", "Claudio Fantacci", "Jose Enrique Chen", "Akhil Raju", "Rae Jeong", "Michael Neunert", ...	Poster	null	We study the problem of robotic stacking with objects of complex geometry. We propose a challenging and diverse set of such objects that was carefully designed to require strategies beyond a simple “pick-and-place” solution. Our method is a reinforcement learning (RL) approach combined with vision-based interactive pol...	[ "sim-to-real", "offline RL", "manipulation", "stacking", "robot learning" ]	null	170	2110.06192	title_snapshot
UD4TAsdE-JM	LEO: Learning Energy-based Models in Factor Graph Optimization	https://openreview.net/forum?id=UD4TAsdE-JM	[ "Paloma Sodhi", "Eric Dexheimer", "Mustafa Mukadam", "Stuart Anderson", "Michael Kaess" ]	Poster	null	We address the problem of learning observation models end-to-end for estimation. Robots operating in partially observable environments must infer latent states from multiple sensory inputs using observation models that capture the joint distribution between latent states and observations. This inference problem can be ...	[ "factor graphs", "energy-based learning", "observation models" ]	null	172	2108.02274	title_snapshot
tCfLLiP7vje	Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects	https://openreview.net/forum?id=tCfLLiP7vje	[ "Haoping Xu", "Yi Ru Wang", "Sagi Eppel", "Alan Aspuru-Guzik", "Florian Shkurti", "Animesh Garg" ]	Oral	null	The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due light refraction and absorption. To tackle the perception challenges posed by transparent objects, we propose TranspareNet, a joint point cloud ...	[ "Transparent Objects", "Depth Completion", "3D Perception", "Data Collection" ]	null	174	2110.00087	title_snapshot
tjdXRqKaz5Y	Aligning an optical interferometer with beam divergence control and continuous action space	https://openreview.net/forum?id=tjdXRqKaz5Y	[ "Stepan Makarenko", "Dmitry Igorevich Sorokin", "Alexander Ulanov", "Alexander Lvovsky" ]	Poster	null	Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups. In this work, we implement vision-based alignment of an optical Mach-Zehnder interferometer with a confocal telescope in one arm, which controls the diameter and divergence of the co...	[ "sim-to-real", "robotics", "optical interferometer" ]	null	178	2107.04457	title_snapshot
I6DLxqk9J0A	Anytime Depth Estimation with Limited Sensing and Computation Capabilities on Mobile Devices	https://openreview.net/forum?id=I6DLxqk9J0A	[ "Yuedong Yang", "Zihui Xue", "Radu Marculescu" ]	Poster	null	Depth estimation is a safety critical and energy sensitive method for environment sensing. However, in real applications, the depth estimation may be halted at any time, due to the random interruptions or low energy capacity of battery when using powerful sensors like 3D LiDAR. To address this problem, we propose a dep...	[ "Depth Estimation", "Anytime Algorithm", "Energy-aware Optimization", "Mobile Devices" ]	null	182	null	null
59aUaAbVfMA	Neural Posterior Domain Randomization	https://openreview.net/forum?id=59aUaAbVfMA	[ "Fabio Muratore", "Theo Gruner", "Florian Wiese", "Boris Belousov", "Michael Gienger", "Jan Peters" ]	Poster	null	Combining domain randomization and reinforcement learning is a widely used approach to obtain control policies that can bridge the gap between simulation and reality. However, existing methods make limiting assumptions on the form of the domain parameter distribution which prevents them from utilizing the full power of...	[ "sim-to-real", "domain randomization", "likelihood-free inference" ]	null	185	null	null
Esw0Wh6Stt5	Fully Autonomous Real-World Reinforcement Learning with Applications to Mobile Manipulation	https://openreview.net/forum?id=Esw0Wh6Stt5	[ "Charles Sun", "Jędrzej Orbik", "Coline Manon Devin", "Brian H Yang", "Abhishek Gupta", "Glen Berseth", "Sergey Levine" ]	Poster	null	In this paper, we study how robots can autonomously learn skills that require a combination of navigation and grasping. Learning robotic skills in the real world remains challenging without large scale data collection and supervision. Our aim is to devise a robotic reinforcement learning system for learning navigation ...	[ "mobile manipulation", "reinforcement learning", "Reset-Free" ]	null	186	2107.13545	title_snapshot
nWLt35BU1z_	Taskography: Evaluating robot task planning over large 3D scene graphs	https://openreview.net/forum?id=nWLt35BU1z_	[ "Christopher Agia", "Krishna Murthy Jatavallabhula", "Mohamed Khodeir", "Ondrej Miksik", "Vibhav Vineet", "Mustafa Mukadam", "Liam Paull", "Florian Shkurti" ]	Poster	null	3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations. However, typical 3DSGs contain hundreds of objects and symbols even for small environments; rendering task planning on the \emph{full} graph impractical. We construct \textbf{Taskography}, the first la...	[ "Robot task planning", "3D scene graphs", "learning to plan", "benchmarks", "planning" ]	null	187	2207.05006	title_snapshot
j3Rguo81Yi_	Specializing Versatile Skill Libraries using Local Mixture of Experts	https://openreview.net/forum?id=j3Rguo81Yi_	[ "Onur Celik", "Dongzhuoran Zhou", "Ge Li", "Philipp Becker", "Gerhard Neumann" ]	Poster	null	A long-cherished vision in robotics is to equip robots with skills that match the versatility and precision of humans. For example, when playing table tennis, a robot should be capable of returning the ball in various ways while precisely placing it at the desired location. A common approach to model such versatile be...	[ "Episodic Policy Search", "Versatile Skill Learning", "Hierarchical RL", "Curriculum Learning" ]	null	190	2112.04216	title_snapshot
csMg2h_LR37	Orientation Probabilistic Movement Primitives on Riemannian Manifolds	https://openreview.net/forum?id=csMg2h_LR37	[ "Leonel Rozo", "Vedant Dave" ]	Poster	null	Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabilistic movement primitives (ProMPs) stand out as a principled approach that models trajectory distributions learned from demonstrations. ProM...	[ "learning from demonstration", "movement primitives", "riemannian manifolds" ]	null	191	2110.15036	title_snapshot
TEQWRlncJVm	Self-supervised Reinforcement Learning with Independently Controllable Subgoals	https://openreview.net/forum?id=TEQWRlncJVm	[ "Andrii Zadaianchuk", "Georg Martius", "Fanny Yang" ]	Poster	null	To successfully tackle challenging manipulation tasks, autonomous agents must learn a diverse set of skills and how to combine them. Recently, self-supervised agents that set their own abstract goals by exploiting the discovered structure in the environment were shown to perform well on many different tasks. In particu...	[ "object-centric representations", "relations", "self-supervised reinforcement learning" ]	null	192	2109.04150	title_snapshot
XEY7ZeEZEjI	Error-Aware Imitation Learning from Teleoperation Data for Mobile Manipulation	https://openreview.net/forum?id=XEY7ZeEZEjI	[ "Josiah Wong", "Albert Tung", "Andrey Kurenkov", "Ajay Mandlekar", "Li Fei-Fei", "Silvio Savarese", "Roberto Martín-Martín" ]	Poster	null	In mobile manipulation (MM), robots can both navigate within and interact with their environment and are thus able to complete many more tasks than robots only capable of navigation or manipulation. In this work, we explore how to apply imitation learning (IL) to learn continuous visuo-motor policies for MM tasks. Much...	[ "Mobile Manipulation", "Imitation Learning", "Error Detection" ]	null	193	2112.05251	title_snapshot
JvXqtLtAtMY	Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks	https://openreview.net/forum?id=JvXqtLtAtMY	[ "Benedikt Mersch", "Xieyuanli Chen", "Jens Behley", "Cyrill Stachniss" ]	Poster	null	Exploiting past 3D LiDAR scans to predict future point clouds is a promising method for autonomous mobile systems to realize foresighted state estimation, collision avoidance, and planning. In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans. Estimating ...	[ "Point Cloud Prediction", "3D LiDAR", "Temporal Convolutional Networks" ]	null	199	2110.04076	title_snapshot
TsqkJJMgHkk	FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy	https://openreview.net/forum?id=TsqkJJMgHkk	[ "Thomas Weng", "Sujay Man Bajracharya", "Yufei Wang", "Khush Agrawal", "David Held" ]	Poster	null	We address the problem of goal-directed cloth manipulation, a challenging task due to the deformability of cloth. Our insight is that optical flow, a technique normally used for motion estimation in video, can also provide an effective representation for corresponding cloth poses across observation and goal images. We ...	[ "deformable object manipulation", "optical flow", "bimanual manipulation", "cloth manipulation" ]	null	202	2111.05623	title_snapshot
xwEaXgFa0MR	AW-Opt: Learning Robotic Skills with Imitation andReinforcement at Scale	https://openreview.net/forum?id=xwEaXgFa0MR	[ "Yao Lu", "Karol Hausman", "Yevgen Chebotar", "Mengyuan Yan", "Eric Jang", "Alexander Herzog", "Ted Xiao", "Alex Irpan", "Mohi Khansari", "Dmitry Kalashnikov", "Sergey Levine" ]	Poster	null	Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amounts of autonomously collected experience. Both methods have complementary strengths and weaknesses: RL can reach a high level of performance, but requires exploration, which c...	[ "reinforcement learning", "imitation learning" ]	null	203	2111.05424	title_judge
wBT0lZJAJ0V	Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction	https://openreview.net/forum?id=wBT0lZJAJ0V	[ "Niklas Funk", "Georgia Chalvatzaki", "Boris Belousov", "Jan Peters" ]	Poster	null	Autonomous robotic assembly requires a well-orchestrated sequence of high-level actions and smooth manipulation executions. Learning to assemble complex 3D structures remains a challenging problem that requires drawing connections between target designs and building blocks, and creating valid assembly sequences conside...	[ "Structured representations", "Autonomous assembly", "Manipulation" ]	null	207	null	null
87_OJU4sw3V	ReSkin: versatile, replaceable, lasting tactile skins	https://openreview.net/forum?id=87_OJU4sw3V	[ "Raunaq Bhirangi", "Tess Hellebrekers", "Carmel Majidi", "Abhinav Gupta" ]	Oral	null	Soft sensors have continued growing interest in robotics, due to their ability to enable both passive conformal contact from the material properties and active contact data from the sensor properties. However, the same properties of conformal contact result in faster deterioration of soft sensors and larger variations ...	[ "Tactile Skin", "Self-supervised Learning", "Magnetic Sensing", "Soft Sensors" ]	null	209	2111.00071	title_snapshot
pXpytHo_GC1	Learning to Plan Optimistically: Uncertainty-Guided Deep Exploration via Latent Model Ensembles	https://openreview.net/forum?id=pXpytHo_GC1	[ "Tim Seyde", "Wilko Schwarting", "Sertac Karaman", "Daniela Rus" ]	Poster	null	Learning complex robot behaviors through interaction requires structured exploration. Planning should target interactions with the potential to optimize long-term performance, while only reducing uncertainty where conducive to this objective. This paper presents Latent Optimistic Value Exploration (LOVE), a strategy t...	[ "Learning Control", "Sample Efficiency", "Exploration" ]	null	212	2010.14641	title_snapshot
JT4orC5QaLQ	Strength Through Diversity: Robust Behavior Learning via Mixture Policies	https://openreview.net/forum?id=JT4orC5QaLQ	[ "Tim Seyde", "Wilko Schwarting", "Igor Gilitschenski", "Markus Wulfmeier", "Daniela Rus" ]	Poster	null	Efficiency in robot learning is highly dependent on hyperparameters. Robot morphology and task structure differ widely and finding the optimal setting typically requires sequential or parallel repetition of experiments, strongly increasing the interaction count. We propose a training method that only relies on a single...	[ "Learning Control", "Hierarchical Optimization", "Sample Efficiency" ]	null	213	null	null
8ZL7Jh1r8WX	Task-Driven Out-of-Distribution Detection with Statistical Guarantees for Robot Learning	https://openreview.net/forum?id=8ZL7Jh1r8WX	[ "Alec Farid", "Sushant Veer", "Anirudha Majumdar" ]	Poster	null	Our goal is to perform out-of-distribution (OOD) detection, i.e., to detect when a robot is operating in environments that are drawn from a different distribution than the environments used to train the robot. We leverage Probably Approximately Correct (PAC)-Bayes theory in order to train a policy with a guaranteed bou...	[ "Out-of-distribution detection", "generalization", "PAC-Bayes" ]	null	217	2106.13703	title_judge
tL24gvaTwhb	Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback	https://openreview.net/forum?id=tL24gvaTwhb	[ "Xiaofei Wang", "Kimin Lee", "Kourosh Hakhamaneshi", "Pieter Abbeel", "Michael Laskin" ]	Poster	null	A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations. However, such generative models inherit the biases of the underlying data and result in poor and unusable skills when trained on imperfect...	[ "Reinforcement Learning", "Skill Extraction", "Human Preferences" ]	null	218	2108.05382	title_snapshot
IScz42A3iCI	Equivariant $Q$ Learning in Spatial Action Spaces	https://openreview.net/forum?id=IScz42A3iCI	[ "Dian Wang", "Robin Walters", "Xupeng Zhu", "Robert Platt" ]	Poster	null	Recently, a variety of new equivariant neural network model architectures have been proposed that generalize better over rotational and reflectional symmetries than standard models. These models are relevant to robotics because many robotics problems can be expressed in a rotationally symmetric way. This paper focuses ...	[ "Reinforcement Learning", "Equivariance", "Manipulation" ]	null	236	2110.15443	title_snapshot
uJi8OvaanP6	Redundancy Resolution as Action Bias in Policy Search for Robotic Manipulation	https://openreview.net/forum?id=uJi8OvaanP6	[ "Firas Al-Hafez", "Jochen J. Steil" ]	Poster	null	We propose a novel approach that biases actions during policy search by lifting the concept of redundancy resolution from multi-DoF robot kinematics to the level of the reward in deep reinforcement learning and evolution strategies. The key idea is to bias the distribution of executed actions in the sense that the imme...	[ "Deep Reinforcement Learning", "Evolution Strategies", "Redundancy Resolution", "Action Bias", "Velocity Control" ]	null	247	null	null
1mDC24WX8Yh	Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear	https://openreview.net/forum?id=1mDC24WX8Yh	[ "Anupam K. Gupta", "Laurence Aitchison", "Nathan F. Lepora" ]	Poster	null	Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the stimulus geometry. In this work, we propose a supervised convolutional deep neural network...	[ "Robotic Touch", "Disentanglement", "Shear", "Object Reconstruction" ]	null	249	2109.03615	title_snapshot
n6xYib0irVR	Influencing Towards Stable Multi-Agent Interactions	https://openreview.net/forum?id=n6xYib0irVR	[ "Woodrow Zhouyuan Wang", "Andy Shih", "Annie Xie", "Dorsa Sadigh" ]	Oral	null	Learning in multi-agent environments is difficult due to the non-stationarity introduced by an opponent's or partner's changing behaviors. Instead of reactively adapting to the other agent's (opponent or partner) behavior, we propose an algorithm to proactively influence the other agent's strategy to stabilize -- which...	[ "multi-agent interactions", "human-robot interaction", "non-stationarity" ]	null	251	2110.08229	title_snapshot
a5ZiDzL0enJ	Learning Inertial Odometry for Dynamic Legged Robot State Estimation	https://openreview.net/forum?id=a5ZiDzL0enJ	[ "Russell Buchanan", "Marco Camurri", "Frank Dellaert", "Maurice Fallon" ]	Poster	null	This paper introduces a novel proprioceptive state estimator for legged robots based on a learned displacement measurement from IMU data. Recent research in pedestrian tracking has shown that motion can be inferred from inertial data using convolutional neural networks. A learned inertial displacement measurement can i...	[ "Legged Robots", "Inertial Navigation", "Deep Neural Networks" ]	null	254	2111.00789	title_snapshot
0f7gUXVAcE9	RICE: Refining Instance Masks in Cluttered Environments with Graph Neural Networks	https://openreview.net/forum?id=0f7gUXVAcE9	[ "Chris Xie", "Arsalan Mousavian", "Yu Xiang", "Dieter Fox" ]	Poster	null	Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have exhibited promising results, they still tend to provide incorrect results in highly cluttered scenes. We postulate that a network architect...	[ "Unseen Object Instance Segmentation", "Graph Neural Network" ]	null	258	2106.15711	title_snapshot
jV0n2wJocXr	CLASP: Constrained Latent Shape Projection for Refining Object Shape from Robot Contact	https://openreview.net/forum?id=jV0n2wJocXr	[ "Brad Saund", "Dmitry Berenson" ]	Poster	null	Robots need both visual and contact sensing to effectively estimate the state of their environment. Camera RGBD data provides rich information of the objects surrounding the robot, and shape priors can help correct noise and fill in gaps and occluded regions. However, when the robot senses unexpected contact, the estim...	[ "Shape Completion", "Contact Sensing" ]	null	268	2110.08719	title_snapshot
Pxs5XwId51n	"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer	https://openreview.net/forum?id=Pxs5XwId51n	[ "Andrew Hundt", "Aditya Murali", "Priyanka Hubli", "Ran Liu", "Nakul Gopalan", "Matthew Gombolay", "Gregory D. Hager" ]	Poster	null	Modern Reinforcement Learning (RL) algorithms are not sample efficient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be repurposed to complete related tasks whe...	[ "Deep Learning in Grasping and Manipulation", "Computer Vision for Robotic Applications", "Imitation Learning", "Reinforcement Learning", "Learning from Demonstration" ]	null	271	null	null
ftOqDUeLPn3	Dealing with the Unknown: Pessimistic Offline Reinforcement Learning	https://openreview.net/forum?id=ftOqDUeLPn3	[ "Jinning Li", "Chen Tang", "Masayoshi Tomizuka", "Wei Zhan" ]	Poster	null	Reinforcement Learning (RL) has been shown effective in domains where the agent can learn policies by actively interacting with its operating environment. However, if we change the RL scheme to offline setting where the agent can only update its policy via static datasets, one of the major issues in offline reinforceme...	[ "Offline Reinforcement Learning", "Out-of-Distribution States" ]	null	274	2111.05440	title_snapshot
EdmeHU4WVjJ	Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR	https://openreview.net/forum?id=EdmeHU4WVjJ	[ "Ziyue Feng", "Longlong Jing", "Peng Yin", "Yingli Tian", "Bing Li" ]	Poster	null	Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose FusionDepth, a novel two-stage network to advance the self-s...	[ "Self-supervised", "Monocular", "Depth Prediction", "Sparse LiDAR" ]	null	278	2109.09628	title_snapshot
PDy45cdhiZ_	Stochastic Policy Optimization with Heuristic Information for Robot Learning	https://openreview.net/forum?id=PDy45cdhiZ_	[ "SEONGHYUN KIM", "Ingook Jang", "Samyeul Noh", "Hyunseok Kim" ]	Poster	null	Stochastic policy-based deep reinforcement learning (RL) approaches have remarkably succeeded to deal with continuous control tasks. However, applying these methods to manipulation tasks remains a challenge since actuators of a robot manipulator require high dimensional continuous action spaces. In this paper, we propo...	[ "Robot manipulation", "Reinforcement learning", "Stochastic policy" ]	null	280	null	null
yt3tDB67lc5	Legged Robot State Estimation using Invariant Kalman Filtering and Learned Contact Events	https://openreview.net/forum?id=yt3tDB67lc5	[ "Tzu-Yuan Lin", "Ray Zhang", "Justin Yu", "Maani Ghaffari" ]	Poster	null	This work develops a learning-based contact estimator for legged robots that bypasses the need for physical sensors and takes multi-modal proprioceptive sensory data as input. Unlike vision-based state estimators, proprioceptive state estimators are agnostic to perceptually degraded situations such as dark or foggy sce...	[ "State Estimation", "Deep Learning", "Legged Robot", "Invariant EKF" ]	null	282	2106.15713	title_snapshot
av44VzmMKwW	Learning Model Preconditions for Planning with Multiple Models	https://openreview.net/forum?id=av44VzmMKwW	[ "Alex Licari LaGrassa", "Oliver Kroemer" ]	Poster	null	Different models can provide differing levels of fidelity when a robot is planning. Analytical models are often fast to evaluate but only work in limited ranges of conditions. Meanwhile, physics simulators are effective at modeling complex interactions between objects but are typically more computationally expensive. L...	[ "manipulation", "planning" ]	null	283	2206.05573	title_snapshot

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Accepted papers for CoRL (Conference on Robot Learning), one dataset per year. • 10 items • Updated 3 days ago