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corl_2023_-3G6_D66Aua | -3G6_D66Aua | corl | 2,023 | Simultaneous Learning of Contact and Continuous Dynamics | Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction lack clear first-principles models and are usually ignored... | Bibit Bianchini;Mathew Halm;Michael Posa | University of Pennsylvania;School of Engineering and Applied Science, University of Pennsylvania;University of Pennsylvania | Poster | main | system identification;dynamics learning;contact-rich manipulation | https://github.com/ebianchi/dair_pll | https://openreview.net/forum?id=-3G6_D66Aua | 14 | Simultaneous Learning of Contact and Continuous Dynamics
Robotic manipulation can greatly benefit from the data efficiency, robustness, and predictability of model-based methods if robots can quickly generate models of novel objects they encounter. This is especially difficult when effects like complex joint friction l... | [
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corl_2023_-HFJuX1uqs | -HFJuX1uqs | corl | 2,023 | Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation | 3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands high-resolution 3D feature grids that are computationally expensive to pro... | Theophile Gervet;Zhou Xian;Nikolaos Gkanatsios;Katerina Fragkiadaki | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Learning from Demonstrations;Manipulation;Transformers | https://github.com/zhouxian/chained-diffuser | https://openreview.net/forum?id=-HFJuX1uqs | 71 | Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation
3D perceptual representations are well suited for robot manipulation as they easily encode occlusions and simplify spatial reasoning. Many manipulation tasks require high spatial precision in end-effector pose prediction, which typically demands h... | [
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corl_2023_-K7-1WvKO3F | -K7-1WvKO3F | corl | 2,023 | ViNT: A Foundation Model for Visual Navigation | General-purpose pre-trained models (``foundation models'') have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typically trained on large and diverse datasets wi... | Dhruv Shah;Ajay Sridhar;Nitish Dashora;Kyle Stachowicz;Kevin Black;Noriaki Hirose;Sergey Levine | UC Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Toyota Central R&D Labs., Inc;Google | Oral | main | visual navigation;multi-task learning;planning;generalization | https://github.com/robodhruv/visualnav-transformer | https://openreview.net/forum?id=-K7-1WvKO3F | 154 | ViNT: A Foundation Model for Visual Navigation
General-purpose pre-trained models (``foundation models'') have enabled practitioners to produce generalizable solutions for individual machine learning problems with datasets that are significantly smaller than those required for learning from scratch. Such models are typ... | [
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corl_2023_09UL1dCqf2n | 09UL1dCqf2n | corl | 2,023 | Preference learning for guiding the tree search in continuous POMDPs | A robot operating in a partially observable environment must perform sensing actions to achieve a goal, such as clearing the objects in front of a shelf to better localize a target object at the back, and estimate its shape for grasping. A POMDP is a principled framework for enabling robots to perform such information-... | Jiyong Ahn;Sanghyeon Son;Dongryung Lee;Jisu Han;Dongwon Son;Beomjoon Kim | Korea Advanced Institute of Science & Technology;;Korea Advanced Institute of Science & Technology;Korea Advanced Institute of Science & Technology;KAIST;Korea Advanced Institute of Science & Technology | Poster | main | POMDP;Online planning;Guided Search;Preference-based learning | https://openreview.net/forum?id=09UL1dCqf2n | 0 | Preference learning for guiding the tree search in continuous POMDPs
A robot operating in a partially observable environment must perform sensing actions to achieve a goal, such as clearing the objects in front of a shelf to better localize a target object at the back, and estimate its shape for grasping. A POMDP is a ... | [
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corl_2023_0I3su3mkuL | 0I3su3mkuL | corl | 2,023 | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions | In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer to provide a scalable representation for Q-functions trained via offline temporal diff... | Yevgen Chebotar;Quan Vuong;Karol Hausman;Fei Xia;Yao Lu;Alex Irpan;Aviral Kumar;Tianhe Yu;Alexander Herzog;Karl Pertsch;Keerthana Gopalakrishnan;Julian Ibarz;Ofir Nachum;Sumedh Anand Sontakke;Grecia Salazar;Huong T Tran;Jodilyn Peralta;Clayton Tan;Deeksha Manjunath;Jaspiar Singh;Brianna Zitkovich;Tomas Jackson;Kanishka... | Google;;;Google;Google;Google DeepMind;University of California, Berkeley;Google Brain;Google;University of Southern California;Research, Google;Google;OpenAI;University of Southern California;;;;;;;;;;Google;Google | Poster | main | Reinforcement Learning;Offline RL;Transformers;Q-Learning;Robotic Manipulation | https://openreview.net/forum?id=0I3su3mkuL | 106 | Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data. Our method uses a Transformer ... | [
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corl_2023_0bZaUfELuW | 0bZaUfELuW | corl | 2,023 | Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control | Our goal is for robots to follow natural language instructions like ``put the towel next to the microwave.'' But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the language instruction, is prohibitive. In contrast, obtaining policies that respond to image goals is mu... | Vivek Myers;Andre Wang He;Kuan Fang;Homer Rich Walke;Philippe Hansen-Estruch;Ching-An Cheng;Mihai Jalobeanu;Andrey Kolobov;Anca Dragan;Sergey Levine | University of California, Berkeley;UC Berkeley, University of California, Berkeley;;University of California, Berkeley;;Microsoft Research;Microsoft Research;Microsoft;University of California, Berkeley;Google | Poster | main | Instruction Following;Representation Learning;Manipulation | https://github.com/rail-berkeley/grif_release | https://openreview.net/forum?id=0bZaUfELuW | 32 | Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control
Our goal is for robots to follow natural language instructions like ``put the towel next to the microwave.'' But getting large amounts of labeled data, i.e. data that contains demonstrations of tasks labeled with the languag... | [
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corl_2023_0hPkttoGAf | 0hPkttoGAf | corl | 2,023 | RVT: Robotic View Transformer for 3D Object Manipulation | For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, we propose RVT, a multi-view transformer for 3D manipulati... | Ankit Goyal;Jie Xu;Yijie Guo;Valts Blukis;Yu-Wei Chao;Dieter Fox | NVIDIA;NVIDIA;University of Michigan;NVIDIA;NVIDIA;Department of Computer Science | Oral | main | 3D Manipulation;Multi-View;Transformer | https://github.com/nvlabs/rvt | https://openreview.net/forum?id=0hPkttoGAf | 140 | RVT: Robotic View Transformer for 3D Object Manipulation
For 3D object manipulation, methods that build an explicit 3D representation perform better than those relying only on camera images. But using explicit 3D representations like voxels comes at large computing cost, adversely affecting scalability. In this work, w... | [
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corl_2023_0hQMcWfjG9 | 0hQMcWfjG9 | corl | 2,023 | $\alpha$-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation | Differentiable Filters are recursive Bayesian estimators that derive the state transition and measurement models from data alone. Their data-driven nature eschews the need for explicit analytical models, while remaining algorithmic components of the filtering process intact. As a result, the gain mechanism -- a critica... | Xiao Liu;Yifan Zhou;Shuhei Ikemoto;Heni Ben Amor | Arizona State University;Arizona State University;Kyushu Institute of Technology;Arizona State University | Poster | main | Differentiable Filters;Sensor Fusion;Multimodal Learning | https://github.com/ir-lab/alpha-MDF | https://openreview.net/forum?id=0hQMcWfjG9 | 8 | $\alpha$-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation
Differentiable Filters are recursive Bayesian estimators that derive the state transition and measurement models from data alone. Their data-driven nature eschews the need for explicit analytical models, while remaining algorit... | [
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corl_2023_0mRSANSzEK | 0mRSANSzEK | corl | 2,023 | Improving Behavioural Cloning with Positive Unlabeled Learning | Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as positive examples; i.e., high-quality demonstrations. Therefore,... | Qiang Wang;Robert McCarthy;David Cordova Bulens;Kevin McGuinness;Noel E. O’Connor;Francisco Roldan Sanchez;Nico Gürtler;Felix Widmaier;Stephen J. Redmond | University College Dublin;;;Dublin City University;;Insight Centre for Data Analytics;Max Planck Institute for Intelligent Systems, Max-Planck Institute;, Max Planck Institute for Intelligent Systems; | Poster | main | Offline policy learning;positive unlabeled learning;behavioural cloning | https://openreview.net/forum?id=0mRSANSzEK | 8 | Improving Behavioural Cloning with Positive Unlabeled Learning
Learning control policies offline from pre-recorded datasets is a promising avenue for solving challenging real-world problems. However, available datasets are typically of mixed quality, with a limited number of the trajectories that we would consider as p... | [
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corl_2023_0o2JgvlzMUc | 0o2JgvlzMUc | corl | 2,023 | Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy | An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot’s ability to learn and adapt at runtime, leading to overly conservative behavior. This paper propos... | Haimin Hu;Zixu Zhang;Kensuke Nakamura;Andrea Bajcsy;Jaime Fernández Fisac | Toyota Research Institute;Princeton University;Princeton University;Princeton University;University of California, Berkeley | Poster | main | Learning-Aware Safety Analysis;Active Information Gathering;Adversarial Reinforcement Learning | https://openreview.net/forum?id=0o2JgvlzMUc | 16 | Deception Game: Closing the Safety-Learning Loop in Interactive Robot Autonomy
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot’s ability to learn a... | [
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corl_2023_2Qrd-Yw4YmF | 2Qrd-Yw4YmF | corl | 2,023 | Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation | Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, capable of seamlessly transitioning between different modes of functionality with... | Yuanpei Chen;Chen Wang;Li Fei-Fei;Karen Liu | South China University of Technology;Computer Science Department, Stanford University;Stanford University;Computer Science Department, Stanford University | Poster | main | Dexterous Manipulation;Reinforcement Learning;Long-Horizon Manipulation | https://github.com/sequential-dexterity/SeqDex | https://openreview.net/forum?id=2Qrd-Yw4YmF | 48 | Sequential Dexterity: Chaining Dexterous Policies for Long-Horizon Manipulation
Many real-world manipulation tasks consist of a series of subtasks that are significantly different from one another. Such long-horizon, complex tasks highlight the potential of dexterous hands, which possess adaptability and versatility, c... | [
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corl_2023_2qKBwyLnln | 2qKBwyLnln | corl | 2,023 | Policy Stitching: Learning Transferable Robot Policies | Training robots with reinforcement learning (RL) typically involves heavy interactions with the environment, and the acquired skills are often sensitive to changes in task environments and robot kinematics. Transfer RL aims to leverage previous knowledge to accelerate learning of new tasks or new body configurations. H... | Pingcheng Jian;Easop Lee;Zachary Bell;Michael M. Zavlanos;Boyuan Chen | Duke University;Duke University;;;Duke University | Poster | main | robot transfer learning;policy stitching | https://github.com/general-robotics-duke/Policy-Stitching | https://openreview.net/forum?id=2qKBwyLnln | 9 | Policy Stitching: Learning Transferable Robot Policies
Training robots with reinforcement learning (RL) typically involves heavy interactions with the environment, and the acquired skills are often sensitive to changes in task environments and robot kinematics. Transfer RL aims to leverage previous knowledge to acceler... | [
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corl_2023_32c8pl84_uD | 32c8pl84_uD | corl | 2,023 | Marginalized Importance Sampling for Off-Environment Policy Evaluation | Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in simulation requires a real-world deployment to assess their performance. This paper proposes a new approach to evaluate the real-world perform... | Pulkit Katdare;Nan Jiang;Katherine Rose Driggs-Campbell | University of Illinois, Urbana Champaign;University of Illinois, Urbana Champaign; | Poster | main | Sim2Real;Policy Evaluation;Robot Validation | https://github.com/pulkitkatdare/mis_off_env_eval | https://openreview.net/forum?id=32c8pl84_uD | 5 | Marginalized Importance Sampling for Off-Environment Policy Evaluation
Reinforcement Learning (RL) methods are typically sample-inefficient, making it challenging to train and deploy RL-policies in real world robots. Even a robust policy trained in simulation requires a real-world deployment to assess their performance... | [
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corl_2023_3gh9hf3R6x | 3gh9hf3R6x | corl | 2,023 | Robot Learning with Sensorimotor Pre-training | We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and actions, we encode the sequence into tokens, mask out a subset, and train a model t... | Ilija Radosavovic;Baifeng Shi;Letian Fu;Ken Goldberg;Trevor Darrell;Jitendra Malik | University of California, Berkeley;University of California, Berkeley;Electrical Engineering & Computer Science Department, University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Electrical Engineering & Computer Science Department | Oral | main | Robot Learning;Self-supervised;Sensorimotor;Pre-training | https://github.com/ir413/rpt | https://openreview.net/forum?id=3gh9hf3R6x | 54 | Robot Learning with Sensorimotor Pre-training
We present a self-supervised sensorimotor pre-training approach for robotics. Our model, called RPT, is a Transformer that operates on sequences of sensorimotor tokens. Given a sequence of camera images, proprioceptive robot states, and actions, we encode the sequence into ... | [
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corl_2023_3uwj8QZROL | 3uwj8QZROL | corl | 2,023 | Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition | We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), we use a large language model (LLM) to guide high-level planning, and sa... | Huy Ha;Pete Florence;Shuran Song | Columbia University;Google;Columbia University | Poster | main | skill learning;language;diffusion | https://github.com/real-stanford/scalingup | https://openreview.net/forum?id=3uwj8QZROL | 170 | Scaling Up and Distilling Down: Language-Guided Robot Skill Acquisition
We present a framework for robot skill acquisition, which 1) efficiently scale up data generation of language-labelled robot data and 2) effectively distills this data down into a robust multi-task language-conditioned visuo-motor policy. For (1), ... | [
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corl_2023_44FPaVRWkbl | 44FPaVRWkbl | corl | 2,023 | DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking | Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directly aggregate features across frames. This work begins with a theoretical and empirical analysis... | Qing LIAN;Tai Wang;Dahua Lin;Jiangmiao Pang | Hong Kong University of Science and Technology;The Chinese University of Hong Kong;Shanghai AI Laboratory ;The Chinese University of Hong Kong | Poster | main | Temporal Modeling;3D Object Detection | https://github.com/OpenRobotLab/DORT | https://openreview.net/forum?id=44FPaVRWkbl | 10 | DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking
Recent multi-camera 3D object detectors usually leverage temporal information to construct multi-view stereo that alleviates the ill-posed depth estimation. However, they typically assume all the objects are static and directl... | [
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corl_2023_48qUHKUEdBf | 48qUHKUEdBf | corl | 2,023 | STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots | Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here, robots must handle object rearrangements, including shifting, removal, and partial occlusion by new items, and track these items after subst... | Yi Li;Muru Zhang;Markus Grotz;Kaichun Mo;Dieter Fox | Department of Computer Science, University of Washington;University of Washington;University of Washington;NVIDIA;Department of Computer Science | Poster | main | Unseen Object Instance Segmentation;Unsupervised Multi Object Tracking;Zero-shot;Discrete Frames | https://sites.google.com/view/stow-corl23 | https://openreview.net/forum?id=48qUHKUEdBf | 6 | STOW: Discrete-Frame Segmentation and Tracking of Unseen Objects for Warehouse Picking Robots
Segmentation and tracking of unseen object instances in discrete frames pose a significant challenge in dynamic industrial robotic contexts, such as distribution warehouses. Here, robots must handle object rearrangements, incl... | [
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corl_2023_4ZK8ODNyFXx | 4ZK8ODNyFXx | corl | 2,023 | Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners | Large language models (LLMs) exhibit a wide range of promising capabilities --- from step-by-step planning to commonsense reasoning --- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this work, we present KnowNo, a framework for measuring and aligning the uncertainty o... | Allen Z. Ren;Anushri Dixit;Alexandra Bodrova;Sumeet Singh;Stephen Tu;Noah Brown;Peng Xu;Leila Takayama;Fei Xia;Jake Varley;Zhenjia Xu;Dorsa Sadigh;Andy Zeng;Anirudha Majumdar | Google DeepMind;California Institute of Technology;Princeton University;Google Brain Robotics;Google;Research, Google;Google;;Google;Google;Columbia University;Stanford University;Princeton University;Google | Oral | main | Language-based planning;uncertainty estimation;conformal prediction | https://github.com/google-research/google-research/tree/master/language_model_uncertainty | https://openreview.net/forum?id=4ZK8ODNyFXx | 248 | Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners
Large language models (LLMs) exhibit a wide range of promising capabilities --- from step-by-step planning to commonsense reasoning --- that may provide utility for robots, but remain prone to confidently hallucinated predictions. In this... | [
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corl_2023_4uFVn6WHyzo | 4uFVn6WHyzo | corl | 2,023 | Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering | Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance t... | Yasasa Abeysirigoonawardena;Kevin Xie;Chuhan Chen;Salar Hosseini Khorasgani;Ruiting Chen;Ruiqi Wang;Florian Shkurti | Department of Computer Science;Department of Computer Science, University of Toronto;Flawless AI Inc.;Toronto University;;Stanford University;University of Toronto | Poster | main | robotics;adversarial attacks;simulation | https://openreview.net/forum?id=4uFVn6WHyzo | 3 | Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering
Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together w... | [
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corl_2023_4x2RUQ99sGz | 4x2RUQ99sGz | corl | 2,023 | Online Model Adaptation with Feedforward Compensation | To cope with distribution shifts or non-stationarity in system dynamics, online adaptation algorithms have been introduced to update offline-learned prediction models in real-time. Existing online adaptation methods focus on optimizing the prediction model by utilizing feedback from the latest prediction error. Unfort... | ABULIKEMU ABUDUWEILI;Changliu Liu | Carnegie Mellon University;Carnegie Mellon University | Poster | main | Online Adaptation;Optimization;Behavior prediction | https://github.com/intelligent-control-lab/Feedforward_Adaptation | https://openreview.net/forum?id=4x2RUQ99sGz | 3 | Online Model Adaptation with Feedforward Compensation
To cope with distribution shifts or non-stationarity in system dynamics, online adaptation algorithms have been introduced to update offline-learned prediction models in real-time. Existing online adaptation methods focus on optimizing the prediction model by utili... | [
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corl_2023_5JMGq83yf1N | 5JMGq83yf1N | corl | 2,023 | Ready, Set, Plan! Planning to Goal Sets Using Generalized Bayesian Inference | Many robotic tasks can have multiple and diverse solutions and, as such, are naturally expressed as goal sets. Examples include navigating to a room, finding a feasible placement location for an object, or opening a drawer enough to reach inside. Using a goal set as a planning objective requires that a model for the ob... | Jana Pavlasek;Stanley Robert Lewis;Balakumar Sundaralingam;Fabio Ramos;Tucker Hermans | University of Michigan;University of Michigan;NVIDIA;NVIDIA;University of Utah | Poster | main | Planning as inference;Variational inference;Nonparametric learning | https://openreview.net/forum?id=5JMGq83yf1N | 4 | Ready, Set, Plan! Planning to Goal Sets Using Generalized Bayesian Inference
Many robotic tasks can have multiple and diverse solutions and, as such, are naturally expressed as goal sets. Examples include navigating to a room, finding a feasible placement location for an object, or opening a drawer enough to reach insi... | [
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corl_2023_69y5fzvaAT | 69y5fzvaAT | corl | 2,023 | RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools | Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous robots remains limited due to challenges in understanding tool-object interac... | Haochen Shi;Huazhe Xu;Samuel Clarke;Yunzhu Li;Jiajun Wu | Stanford University;Tsinghua University;;Stanford University;Stanford University | Oral | main | Deformable Object Manipulation;Long-horizon Planning;Model Learning;Tool Usage | https://github.com/hshi74/robocook | https://openreview.net/forum?id=69y5fzvaAT | 62 | RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools
Humans excel in complex long-horizon soft body manipulation tasks via flexible tool use: bread baking requires a knife to slice the dough and a rolling pin to flatten it. Often regarded as a hallmark of human cognition, tool use in autonomous ... | [
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corl_2023_6Um8P8Fvyhl | 6Um8P8Fvyhl | corl | 2,023 | MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning | We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent offline model-free approaches successfully use online fine-tuning to either improve the performance of the agent over the data collection poli... | Rafael Rafailov;Kyle Beltran Hatch;Victor Kolev;John D Martin;Mariano Phielipp;Chelsea Finn | Stanford University;Toyota Research Institute;Stanford University;Intel;Intel Labs;Google | Poster | main | offline RL;online fine-tuning;model-learning;robot learning | https://openreview.net/forum?id=6Um8P8Fvyhl | 16 | MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent offline model-free approaches successfully use online fine-tun... | [
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corl_2023_6a4sECAMCA | 6a4sECAMCA | corl | 2,023 | Learning to Drive Anywhere | Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains, \ie, without accounting f... | Ruizhao Zhu;Peng Huang;Eshed Ohn-Bar;Venkatesh Saligrama | Boston University;Boston University;Boston University;Boston University | Poster | main | Global-scale Autonomous Driving;Imitation Learning;Transformer | https://openreview.net/forum?id=6a4sECAMCA | 9 | Learning to Drive Anywhere
Human drivers can seamlessly adapt their driving decisions across geographical locations with diverse conditions and rules of the road, e.g., left vs. right-hand traffic. In contrast, existing models for autonomous driving have been thus far only deployed within restricted operational domains... | [
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corl_2023_6kSohKYYTn0 | 6kSohKYYTn0 | corl | 2,023 | Measuring Interpretability of Neural Policies of Robots with Disentangled Representation | The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucial since robots are mostly safety-critical systems. This urges a formal and quantitative u... | Tsun-Hsuan Wang;Wei Xiao;Tim Seyde;Ramin Hasani;Daniela Rus | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Oral | main | Interpretability;Disentangled Representation;Neural Policy | https://github.com/zswang666/interpret-by-disentangle | https://openreview.net/forum?id=6kSohKYYTn0 | 8 | Measuring Interpretability of Neural Policies of Robots with Disentangled Representation
The advancement of robots, particularly those functioning in complex human-centric environments, relies on control solutions that are driven by machine learning. Understanding how learning-based controllers make decisions is crucia... | [
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corl_2023_6zGpfOBImD | 6zGpfOBImD | corl | 2,023 | M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place | With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation. These generic models are able to interpret complex tasks using language commands, but they often have difficulties generalizing to out-of-distribution obje... | Wentao Yuan;Adithyavairavan Murali;Arsalan Mousavian;Dieter Fox | University of Washington, Seattle;;NVIDIA;Department of Computer Science | Poster | main | Object manipulation;Multi-task learning;Pick and place | https://m2-t2.github.io | https://openreview.net/forum?id=6zGpfOBImD | 23 | M2T2: Multi-Task Masked Transformer for Object-centric Pick and Place
With the advent of large language models and large-scale robotic datasets, there has been tremendous progress in high-level decision-making for object manipulation. These generic models are able to interpret complex tasks using language commands, but... | [
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corl_2023_770xKAHeFS | 770xKAHeFS | corl | 2,023 | How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations | We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs)---SDEs whose drift and diffusion terms are both parametrized by neural networks. We construct the drift term to leverage a priori physics knowledge as inductive bias, and we design the diffusi... | Franck Djeumou;Cyrus Neary;ufuk topcu | The University of Texas at Austin;University of Texas, Austin;University of Texas, Austin | Oral | main | Neural SDE;Physics-Informed Learning;Data-Driven Modeling;Dynamical Systems;Control;Model-Based Reinforcement Learning | https://github.com/wuwushrek/sde4mbrl | https://openreview.net/forum?id=770xKAHeFS | 13 | How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations
We present a framework and algorithms to learn controlled dynamics models using neural stochastic differential equations (SDEs)---SDEs whose drift and diffusion terms are both param... | [
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corl_2023_7CtUcT_OHmC | 7CtUcT_OHmC | corl | 2,023 | Learning Human Contribution Preferences in Collaborative Human-Robot Tasks | In human-robot collaboration, both human and robotic agents must work together to achieve a set of shared objectives. However, each team member may have individual preferences, or constraints, for how they would like to contribute to the task. Effective teams align their actions to optimize task performance while satis... | Michelle D Zhao;Reid Simmons;Henny Admoni | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | human-robot collaboration;reward learning;human-robot interaction | https://openreview.net/forum?id=7CtUcT_OHmC | 10 | Learning Human Contribution Preferences in Collaborative Human-Robot Tasks
In human-robot collaboration, both human and robotic agents must work together to achieve a set of shared objectives. However, each team member may have individual preferences, or constraints, for how they would like to contribute to the task. E... | [
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corl_2023_7Pkzm2FgUmq | 7Pkzm2FgUmq | corl | 2,023 | SLAP: Spatial-Language Attention Policies | Despite great strides in language-guided manipulation, existing work has been constrained to table-top settings. Table-tops allow for perfect and consistent camera angles, properties are that do not hold in mobile manipulation. Task plans that involve moving around the environment must be robust to egocentric views and... | Priyam Parashar;Vidhi Jain;Xiaohan Zhang;Jay Vakil;Sam Powers;Yonatan Bisk;Chris Paxton | Meta Facebook;Google;State University of New York at Binghamton;Meta AI ;Carnegie Mellon University;Meta;Meta Platforms | Poster | main | learning from demonstration;language-based robotics | https://github.com/facebookresearch/home-robot/tree/main/projects/slap_manipulation | https://openreview.net/forum?id=7Pkzm2FgUmq | 8 | SLAP: Spatial-Language Attention Policies
Despite great strides in language-guided manipulation, existing work has been constrained to table-top settings. Table-tops allow for perfect and consistent camera angles, properties are that do not hold in mobile manipulation. Task plans that involve moving around the environm... | [
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corl_2023_7TYeO2XVqI | 7TYeO2XVqI | corl | 2,023 | SayTap: Language to Quadrupedal Locomotion | Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or motor torques. This paper proposes an approach to use foot contact patterns as an interface that bridges human commands in na... | Yujin Tang;Wenhao Yu;Jie Tan;Heiga Zen;Aleksandra Faust;Tatsuya Harada | Google;Google;Google;Google;Google Brain;The University of Tokyo | Poster | main | Large language model (LLM);Quadrupedal robots;Locomotion | https://openreview.net/forum?id=7TYeO2XVqI | 45 | SayTap: Language to Quadrupedal Locomotion
Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or motor torques. This paper proposes an approach to use foot contact patterns as an ... | [
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corl_2023_86aMPJn6hX9F | 86aMPJn6hX9F | corl | 2,023 | Stabilize to Act: Learning to Coordinate for Bimanual Manipulation | Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is immense, constructing control policies for dual arm autonomous systems brings inherent difficulties. One such difficulty is the high-dimensional... | Jennifer Grannen;Yilin Wu;Brandon Vu;Dorsa Sadigh | Computer Science Department, Stanford University;Stanford University;Computer Science Department, Stanford University;Stanford University | Oral | main | Bimanual Manipulation;Learning from Demonstrations;Deformable Object Manipulation | https://openreview.net/forum?id=86aMPJn6hX9F | 33 | Stabilize to Act: Learning to Coordinate for Bimanual Manipulation
Key to rich, dexterous manipulation in the real world is the ability to coordinate control across two hands. However, while the promise afforded by bimanual robotic systems is immense, constructing control policies for dual arm autonomous systems brings... | [
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corl_2023_8L6pHd9aS6w | 8L6pHd9aS6w | corl | 2,023 | XSkill: Cross Embodiment Skill Discovery | Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior. However, directly extracting reusable robot manipulation skills from unstructured human videos is challenging due to the big embodiment difference and unobserved action para... | Mengda Xu;Zhenjia Xu;Cheng Chi;Manuela Veloso;Shuran Song | Columbia University;Columbia University;Columbia University;School of Computer Science, Carnegie Mellon University;Columbia University | Poster | main | Manipulation;Representation Learning;Cross-Embodiements | https://xskill.cs.columbia.edu/ | https://openreview.net/forum?id=8L6pHd9aS6w | 70 | XSkill: Cross Embodiment Skill Discovery
Human demonstration videos are a widely available data source for robot learning and an intuitive user interface for expressing desired behavior. However, directly extracting reusable robot manipulation skills from unstructured human videos is challenging due to the big embodim... | [
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corl_2023_8asqEWO479I | 8asqEWO479I | corl | 2,023 | Push Past Green: Learning to Look Behind Plant Foliage by Moving It | Autonomous agriculture applications (e.g., inspection, phenotyping, plucking fruits) require manipulating the plant foliage to look behind the leaves and the branches. Partial visibility, extreme clutter, thin structures, and unknown geometry and dynamics for plants make such manipulation challenging. We tackle these ... | Xiaoyu Zhang;Saurabh Gupta | Department of Computer Science;University of Illinois, Urbana Champaign | Poster | main | Deformable Object Manipulation;Model-building;Self-supervision | https://github.com/ErinZhang1998/pushpastgreen | https://openreview.net/forum?id=8asqEWO479I | 3 | Push Past Green: Learning to Look Behind Plant Foliage by Moving It
Autonomous agriculture applications (e.g., inspection, phenotyping, plucking fruits) require manipulating the plant foliage to look behind the leaves and the branches. Partial visibility, extreme clutter, thin structures, and unknown geometry and dyna... | [
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corl_2023_8scj3Y0RLq | 8scj3Y0RLq | corl | 2,023 | Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies | Dynamical System (DS) based Learning from Demonstration (LfD) allows learning of reactive motion policies with stability and convergence guarantees from a few trajectories. Yet, current DS learning techniques lack the flexibility to generalize to new task instances as they overlook explicit task parameters that inheren... | Tianyu Li;Nadia Figueroa | University of Pennsylvania;University of Pennsylvania | Poster | main | Stable Dynamical Systems;Reactive Motion Policies;Learning from Demonstrations;Task Parametrization;Task Generalization | https://openreview.net/forum?id=8scj3Y0RLq | 7 | Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies
Dynamical System (DS) based Learning from Demonstration (LfD) allows learning of reactive motion policies with stability and convergence guarantees from a few trajectories. Yet, current DS learning techniques lack the flexibility... | [
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corl_2023_8yTS_nAILxt | 8yTS_nAILxt | corl | 2,023 | REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction | The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong reasoning abilities on textual inputs. To leverage the power of LLMs for robot failure explanation, we introduce REFLECT, a framework... | Zeyi Liu;Arpit Bahety;Shuran Song | Columbia University;Columbia University;Columbia University | Poster | main | Large Language Model;Explainable AI;Task Planning | https://github.com/real-stanford/reflect | https://openreview.net/forum?id=8yTS_nAILxt | 136 | REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction
The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong reasoning abilities on textual inputs. To leverage the ... | [
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corl_2023_93qz1k6_6h | 93qz1k6_6h | corl | 2,023 | Dexterous Functional Grasping | While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real world. The main benefit of dexterous hands over two-fingered ones is their ability to pickup tools and other objects (including thin ones) and... | Ananye Agarwal;Shagun Uppal;Kenneth Shaw;Deepak Pathak | Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Functional Grasping;Sim2real | https://openreview.net/forum?id=93qz1k6_6h | 34 | Dexterous Functional Grasping
While there have been significant strides in dexterous manipulation, most of it is limited to benchmark tasks like in-hand reorientation which are of limited utility in the real world. The main benefit of dexterous hands over two-fingered ones is their ability to pickup tools and other obj... | [
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corl_2023_9GRE34K0SB | 9GRE34K0SB | corl | 2,023 | AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer | Simulation parameter settings such as contact models and object geometry approximations are critical to training robust manipulation policies capable of transferring from simulation to real-world deployment. There is often an irreducible gap between simulation and reality: attempting to match the dynamics between simul... | Allen Z. Ren;Hongkai Dai;Benjamin Burchfiel;Anirudha Majumdar | Google DeepMind;Toyota Research Institute;Dexterous Manipulation Group, Toyota Research Institute;Princeton University | Poster | main | Contact-rich manipulation;sim-to-real transfer | https://github.com/irom-lab/AdaptSim | https://openreview.net/forum?id=9GRE34K0SB | 16 | AdaptSim: Task-Driven Simulation Adaptation for Sim-to-Real Transfer
Simulation parameter settings such as contact models and object geometry approximations are critical to training robust manipulation policies capable of transferring from simulation to real-world deployment. There is often an irreducible gap between s... | [
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corl_2023_9SM6l0HyY_ | 9SM6l0HyY_ | corl | 2,023 | Learning Generalizable Manipulation Policies with Object-Centric 3D Representations | We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors. GROOT builds policies that generalize beyond their initial training conditions for vision-based manipulation. It constructs object-centric 3D representations that are robust toward background changes and cam... | Yifeng Zhu;Zhenyu Jiang;Peter Stone;Yuke Zhu | The University of Texas at Austin;University of Texas, Austin;University of Texas, Austin;Computer Science Department, University of Texas, Austin | Poster | main | robot manipulation;imitation learning;object-centric representations | https://github.com/UT-Austin-RPL/GROOT | https://openreview.net/forum?id=9SM6l0HyY_ | 48 | Learning Generalizable Manipulation Policies with Object-Centric 3D Representations
We introduce GROOT, an imitation learning method for learning robust policies with object-centric and 3D priors. GROOT builds policies that generalize beyond their initial training conditions for vision-based manipulation. It constructs... | [
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0.0048142527230083... | |
corl_2023_9_8LF30mOC | 9_8LF30mOC | corl | 2,023 | VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models | Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major... | Wenlong Huang;Chen Wang;Ruohan Zhang;Yunzhu Li;Jiajun Wu;Li Fei-Fei | Stanford University;Computer Science Department, Stanford University;Stanford University;Stanford University;Stanford University;Stanford University | Oral | main | Manipulation;Large Language Models;Model-based Planning | https://github.com/huangwl18/VoxPoser | https://openreview.net/forum?id=9_8LF30mOC | 564 | VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives t... | [
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corl_2023_9al6taqfTzr | 9al6taqfTzr | corl | 2,023 | Open-World Object Manipulation using Pre-Trained Vision-Language Models | For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary, e.g. ``can you get me the pink stuffed whale?'' to their sensory observations and actions. This brings up a notably difficult challenge for robots: while robot learning approaches allow robots ... | Austin Stone;Ted Xiao;Yao Lu;Keerthana Gopalakrishnan;Kuang-Huei Lee;Quan Vuong;Paul Wohlhart;Sean Kirmani;Brianna Zitkovich;Fei Xia;Chelsea Finn;Karol Hausman | Google;;Google;Research, Google;Google;;Graz University of Technology;Google X;;Google;Google; | Poster | main | https://openreview.net/forum?id=9al6taqfTzr | 161 | Open-World Object Manipulation using Pre-Trained Vision-Language Models
For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary, e.g. ``can you get me the pink stuffed whale?'' to their sensory observations and actions. This brings up a notably diffi... | [
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corl_2023_9bK38pUBzU | 9bK38pUBzU | corl | 2,023 | Language-Conditioned Path Planning | Contact is at the core of robotic manipulation. At times, it is desired (e.g. manipulation and grasping), and at times, it is harmful (e.g. when avoiding obstacles). However, traditional path planning algorithms focus solely on collision-free paths, limiting their applicability in contact-rich tasks. To address this li... | Amber Xie;Youngwoon Lee;Pieter Abbeel;Stephen James | ;University of California, Berkeley;Covariant;Dyson | Poster | main | Robotic Manipulation;Path Planning;Collision Avoidance;Learned Collision Function | https://github.com/amberxie88/lapp | https://openreview.net/forum?id=9bK38pUBzU | 16 | Language-Conditioned Path Planning
Contact is at the core of robotic manipulation. At times, it is desired (e.g. manipulation and grasping), and at times, it is harmful (e.g. when avoiding obstacles). However, traditional path planning algorithms focus solely on collision-free paths, limiting their applicability in con... | [
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corl_2023_9cTEQWMo1BF | 9cTEQWMo1BF | corl | 2,023 | LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds | A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage “auto-labelling” offboard perception models that are trained to automatically generate annotations from raw LiDAR point clouds at a fraction of the cost... | Anqi Joyce Yang;Sergio Casas;Nikita Dvornik;Sean Segal;Yuwen Xiong;Jordan Sir Kwang Hu;Carter Fang;Raquel Urtasun | Waabi Innovation Inc;Waabi;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Waabi Innovation Inc.;;Department of Computer Science, University of Toronto;University of Toronto | Poster | main | Auto-labelling;Offboard Perception;Trajectory Refinement;Transformer | https://openreview.net/forum?id=9cTEQWMo1BF | 8 | LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage “auto-labelling” offboard perception models that are trained to... | [
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corl_2023_AIgm8ZE_DlD | AIgm8ZE_DlD | corl | 2,023 | A Universal Semantic-Geometric Representation for Robotic Manipulation | Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing precise spatial information. On the other side, depth cameras offer critical 3D geometry data but capture limited semantics. Therefore, int... | Tong Zhang;Yingdong Hu;Hanchen Cui;Hang Zhao;Yang Gao | Tsinghua University;Tsinghua University;Shanghai Qi Zhi Institute;Tsinghua University;Tsinghua University | Poster | main | Representation Learning;Robotic Manipulation | https://openreview.net/forum?id=AIgm8ZE_DlD | 22 | A Universal Semantic-Geometric Representation for Robotic Manipulation
Robots rely heavily on sensors, especially RGB and depth cameras, to perceive and interact with the world. RGB cameras record 2D images with rich semantic information while missing precise spatial information. On the other side, depth cameras offer ... | [
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corl_2023_ANJuNDFdvP | ANJuNDFdvP | corl | 2,023 | UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding | This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfolding and folding decisions into a single policy model that is adaptable to different garm... | Han Xue;Yutong Li;Wenqiang Xu;Huanyu Li;Dongzhe Zheng;Cewu Lu | Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;;Shanghai Jiaotong University;Shanghai Jiaotong University | Poster | main | Deformable Object Manipulation;Bimanual Manipulation;Garment Folding | https://github.com/xiaoxiaoxh/UniFolding | https://openreview.net/forum?id=ANJuNDFdvP | 15 | UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding
This paper explores the development of UniFolding, a sample-efficient, scalable, and generalizable robotic system for unfolding and folding various garments. UniFolding employs the proposed UFONet neural network to integrate unfo... | [
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corl_2023_AnDDMQgM7- | AnDDMQgM7- | corl | 2,023 | Equivariant Reinforcement Learning under Partial Observability | Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning. Specifically, by encoding the equivariance regarding specific... | Hai Huu Nguyen;Andrea Baisero;David Klee;Dian Wang;Robert Platt;Christopher Amato | Northeastern University;Northeastern University;Boston Dynamics Artificial Intelligence Institute;Boston Dynamics AI Institute;Northeastern University;Northeastern University | Poster | main | Partial Observability;Equivariant Learning;Symmetry | https://github.com/hai-h-nguyen/equi-rl-for-pomdps | https://openreview.net/forum?id=AnDDMQgM7- | 15 | Equivariant Reinforcement Learning under Partial Observability
Incorporating inductive biases is a promising approach for tackling challenging robot learning domains with sample-efficient solutions. This paper identifies partially observable domains where symmetries can be a useful inductive bias for efficient learning... | [
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corl_2023_ApxLUk8U-l | ApxLUk8U-l | corl | 2,023 | Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning | In imitation and reinforcement learning (RL), the cost of human supervision limits the amount of data that the robots can be trained on. While RL offers a framework for building self-improving robots that can learn via trial-and-error autonomously, practical realizations end up requiring extensive human supervision for... | Archit Sharma;Ahmed M Ahmed;Rehaan Ahmad;Chelsea Finn | Stanford University;;Computer Science Department, Stanford University;Google | Poster | main | reinforcement learning;autonomous;reset-free;manipulation | https://github.com/rehaanahmad2013/self-improving-robots/ | https://openreview.net/forum?id=ApxLUk8U-l | 16 | Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
In imitation and reinforcement learning (RL), the cost of human supervision limits the amount of data that the robots can be trained on. While RL offers a framework for building self-improving robots that can learn via trial-and-error autono... | [
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corl_2023_AyRr_i028w | AyRr_i028w | corl | 2,023 | Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States | Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this work, we explore the feasibility of plans produced by LfD. As in prior work, w... | Zidan Wang;Takeru Oba;Takuma Yoneda;Rui Shen;Matthew Walter;Bradly C. Stadie | Northwestern University;Toyota Technological Institute;Toyota Technological Institute at Chicago;Yale University;Toyota Technological Institute at Chicago;Northwestern University | Poster | main | Imitation Learning;Reinforcement Learning;Diffusion;Cold Diffusion;Planning;Safety | https://github.com/zidanwang2025/cold_diffusion_on_replay_buffer | https://openreview.net/forum?id=AyRr_i028w | 6 | Cold Diffusion on the Replay Buffer: Learning to Plan from Known Good States
Learning from demonstrations (LfD) has successfully trained robots to exhibit remarkable generalization capabilities. However, many powerful imitation techniques do not prioritize the feasibility of the robot behaviors they generate. In this w... | [
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corl_2023_B7PnAw4ze0l | B7PnAw4ze0l | corl | 2,023 | Precise Robotic Needle-Threading with Tactile Perception and Reinforcement Learning | This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: \textit{Tail-end Finding} and \textit{Tail-end Insertion}. In the first stage, the agent traces the ... | Zhenjun Yu;Wenqiang Xu;Siqiong Yao;Jieji Ren;Tutian Tang;Yutong Li;Guoying Gu;Cewu Lu | Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University;Shanghai Jiaotong University | Poster | main | tactile perception;needle threading | https://openreview.net/forum?id=B7PnAw4ze0l | 8 | Precise Robotic Needle-Threading with Tactile Perception and Reinforcement Learning
This work presents a novel tactile perception-based method, named T-NT, for performing the needle-threading task, an application of deformable linear object (DLO) manipulation. This task is divided into two main stages: \textit{Tail-end... | [
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corl_2023_BimpCf1rT7 | BimpCf1rT7 | corl | 2,023 | Compositional Diffusion-Based Continuous Constraint Solvers | This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then rejecting the value assignments when other constraints are viola... | Zhutian Yang;Jiayuan Mao;Yilun Du;Jiajun Wu;Joshua B. Tenenbaum;Tomás Lozano-Pérez;Leslie Pack Kaelbling | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Stanford University;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Diffusion Models;Constraint Satisfaction Problems;Task and Motion Planning | https://openreview.net/forum?id=BimpCf1rT7 | 29 | Compositional Diffusion-Based Continuous Constraint Solvers
This paper introduces an approach for learning to solve continuous constraint satisfaction problems (CCSP) in robotic reasoning and planning. Previous methods primarily rely on hand-engineering or learning generators for specific constraint types and then reje... | [
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corl_2023_BzjLaVvr955 | BzjLaVvr955 | corl | 2,023 | Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning | To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Flows (NFs). However, we find that prior work with NFs attempts to match the complex targe... | Jianxiang Feng;Jongseok Lee;Simon Geisler;Stephan Günnemann;Rudolph Triebel | RMC, German Aerospace Center (DLR);German Aerospace Center (DLR);Technical University Munich;Technical University Munich;Technical University Munich | Poster | main | Normalizing Flows;Out-of-Distribution Detection;Robotic Introspection | https://github.com/DLR-RM | https://openreview.net/forum?id=BzjLaVvr955 | 6 | Topology-Matching Normalizing Flows for Out-of-Distribution Detection in Robot Learning
To facilitate reliable deployments of autonomous robots in the real world, Out-of-Distribution (OOD) detection capabilities are often required. A powerful approach for OOD detection is based on density estimation with Normalizing Fl... | [
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corl_2023_C5MQUlzhVjQ | C5MQUlzhVjQ | corl | 2,023 | Surrogate Assisted Generation of Human-Robot Interaction Scenarios | As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmically generated diverse scenarios that reveal system failures in a shar... | Varun Bhatt;Heramb Nemlekar;Matthew Christopher Fontaine;Bryon Tjanaka;Hejia Zhang;Ya-Chuan Hsu;Stefanos Nikolaidis | University of Southern California;University of Southern California;University of Southern California;InstaDeep;University of Southern California;University of Southern California;University of Southern California | Oral | main | Scenario Generation;Human-Robot Interaction;Quality Diversity | https://github.com/icaros-usc/dsas | https://openreview.net/forum?id=C5MQUlzhVjQ | 11 | Surrogate Assisted Generation of Human-Robot Interaction Scenarios
As human-robot interaction (HRI) systems advance, so does the difficulty of evaluating and understanding the strengths and limitations of these systems in different environments and with different users. To this end, previous methods have algorithmicall... | [
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corl_2023_CnKf9TyYtf2 | CnKf9TyYtf2 | corl | 2,023 | Few-Shot In-Context Imitation Learning via Implicit Graph Alignment | Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a class makes it difficult to infer the task-relevant relationship betwe... | Vitalis Vosylius;Edward Johns | Imperial College London;Imperial College London | Poster | main | Few-Shot Imitation Learning;Graph Neural Networks | https://openreview.net/forum?id=CnKf9TyYtf2 | 11 | Few-Shot In-Context Imitation Learning via Implicit Graph Alignment
Consider the following problem: given a few demonstrations of a task across a few different objects, how can a robot learn to perform that same task on new, previously unseen objects? This is challenging because the large variety of objects within a cl... | [
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corl_2023_D0X97ODIYK | D0X97ODIYK | corl | 2,023 | Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning | Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primarily on single-level rearrangement planning and, even if multiple levels exist, dependency relations among substructures are geometrically simp... | Manav Kulshrestha;Ahmed H Qureshi | Purdue University;Purdue University | Poster | main | Rearrangement Planning;Robot Manipulation;Graph Attention | https://openreview.net/forum?id=D0X97ODIYK | 6 | Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning
Robotic manipulation tasks, such as object rearrangement, play a crucial role in enabling robots to interact with complex and arbitrary environments. Existing work focuses primarily on single-level rearrangement planning and, even if... | [
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corl_2023_DYPOvNot5F | DYPOvNot5F | corl | 2,023 | Diff-LfD: Contact-aware Model-based Learning from Visual Demonstration for Robotic Manipulation via Differentiable Physics-based Simulation and Rendering | Learning from Demonstration (LfD) is an efficient technique for robots to acquire new skills through expert observation, significantly mitigating the need for laborious manual reward function design. This paper introduces a novel framework for model-based LfD in the context of robotic manipulation. Our proposed pipelin... | Xinghao Zhu;Jinghan Ke;Zhixuan Xu;Zhixin Sun;Bizhe Bai;Jun Lv;Qingtao Liu;Yuwei Zeng;Qi Ye;Cewu Lu;Masayoshi Tomizuka;Lin Shao | ;;Zhejiang University;Nanjing University;University of Queensland;Shanghai Jiaotong University;Zhejiang University;National University of Singapore;Zhejiang University;Shanghai Jiaotong University;;National University of Singapore | Oral | main | Learning from Demonstration;model-based robot learning;differentiable physics-based simulation and rendering | https://openreview.net/forum?id=DYPOvNot5F | 19 | Diff-LfD: Contact-aware Model-based Learning from Visual Demonstration for Robotic Manipulation via Differentiable Physics-based Simulation and Rendering
Learning from Demonstration (LfD) is an efficient technique for robots to acquire new skills through expert observation, significantly mitigating the need for laborio... | [
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corl_2023_E2vL12SwO1 | E2vL12SwO1 | corl | 2,023 | PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training | Brain-body co-design, which involves the collaborative design of control strategies and morphologies, has emerged as a promising approach to enhance a robot's adaptability to its environment. However, the conventional co-design process often starts from scratch, lacking the utilization of prior knowledge. This can resu... | Yuxing Wang;Shuang Wu;Tiantian Zhang;Yongzhe Chang;Haobo Fu;QIANG FU;Xueqian Wang | Tsinghua University;Tsinghua University;Tsinghua University;Tencent AI Lab;Tencent AI Lab;Tsinghua University;Tencent AI Lab | Oral | main | Robot Co-design;Pre-training;Reinforcement Learning;Modular Soft Robots | https://openreview.net/forum?id=E2vL12SwO1 | 9 | PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training
Brain-body co-design, which involves the collaborative design of control strategies and morphologies, has emerged as a promising approach to enhance a robot's adaptability to its environment. However, the conventional co-des... | [
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corl_2023_ES_TOp4YJeD | ES_TOp4YJeD | corl | 2,023 | ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation | Monocular depth estimation is challenging due to its inherent ambiguity and ill-posed nature, yet it is quite important to many applications. While recent works achieve limited accuracy by designing increasingly complicated networks to extract features with limited spatial geometric cues from a single RGB image, we int... | ZiZhang Wu;Zhuozheng Li;Zhi-Gang Fan;Yunzhe Wu;Xiaoquan Wang;Rui Tang;Jian Pu | Fudan University;ZongMu Technology Co.,Ltd.;;ZongmuTech;Zongmutech;;Fudan University | Poster | main | Monocular depth estimation;Camera perception;Distillation | https://openreview.net/forum?id=ES_TOp4YJeD | 2 | ADU-Depth: Attention-based Distillation with Uncertainty Modeling for Depth Estimation
Monocular depth estimation is challenging due to its inherent ambiguity and ill-posed nature, yet it is quite important to many applications. While recent works achieve limited accuracy by designing increasingly complicated networks ... | [
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corl_2023_EXQ0eXtX3OW | EXQ0eXtX3OW | corl | 2,023 | Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play | Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either operate on visual observations or state estimates derived from vision. However, such methods perform poorly on fine-grained manipulation tas... | Irmak Guzey;Ben Evans;Soumith Chintala;Lerrel Pinto | New York University;Meta Facebook;New York University;New York University | Poster | main | Tactile;Dexterity;Manipulation | https://github.com/irmakguzey/tactile-dexterity | https://openreview.net/forum?id=EXQ0eXtX3OW | 64 | Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play
Teaching dexterity to multi-fingered robots has been a longstanding challenge in robotics. Most prominent work in this area focuses on learning controllers or policies that either operate on visual observations or state esti... | [
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corl_2023_EvuAJ0wD98 | EvuAJ0wD98 | corl | 2,023 | Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning | Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we introduce a distributed multi-agent reinforcement learning (MARL) algorithm for joint trajectory and... | Xiyang Wu;Rohan Chandra;Tianrui Guan;Amrit Bedi;Dinesh Manocha | University of Maryland, College Park;University of Texas at Austin;Department of Computer Science, University of Maryland, College Park;University of Maryland, College Park;University of Maryland, College Park | Oral | main | Autonomous Driving;Multi-agent Reinforcement Learning;Representation Learning | https://openreview.net/forum?id=EvuAJ0wD98 | 10 | Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Navigating safely and efficiently in dense and heterogeneous traffic scenarios is challenging for autonomous vehicles (AVs) due to their inability to infer the behaviors or intentions of nearby drivers. In this work, we in... | [
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corl_2023_Eyb4e3GBuuR | Eyb4e3GBuuR | corl | 2,023 | Energy-based Potential Games for Joint Motion Forecasting and Control | This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control. Despite its interpretability, applying game theory to real-world robotics, like automated driving, faces challenges such as unknown game parameters. To tackle these, we establish a conne... | Christopher Diehl;Tobias Klosek;Martin Krueger;Nils Murzyn;Timo Osterburg;Torsten Bertram | TU Dortmund University;Technische Universität Dortmund;TU Dortmund University;;Technische Universität Dortmund; | Poster | main | Trajectory Prediction;Multi-Agent Interaction;Game-Theoretic Motion Planning;Energy-based Model;Optimal Control;Autonomous Vehicles | https://github.com/rst-tu-dortmund/diff_epo_planner | https://openreview.net/forum?id=Eyb4e3GBuuR | 8 | Energy-based Potential Games for Joint Motion Forecasting and Control
This work uses game theory as a mathematical framework to address interaction modeling in multi-agent motion forecasting and control. Despite its interpretability, applying game theory to real-world robotics, like automated driving, faces challenges ... | [
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corl_2023_FRKBdXhkQE0 | FRKBdXhkQE0 | corl | 2,023 | FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing | We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains autonomously in the real world, without human interventions, and without requiring any simulation or expert demonstrations. Our system integrat... | Kyle Stachowicz;Dhruv Shah;Arjun Bhorkar;Ilya Kostrikov;Sergey Levine | University of California, Berkeley;UC Berkeley;University of California, Berkeley;University of California, Berkeley;Google | Poster | main | reinforcement learning;offroad driving;vision-based navigation | github.com/kylestach/fastrlap-release | https://openreview.net/forum?id=FRKBdXhkQE0 | 27 | FastRLAP: A System for Learning High-Speed Driving via Deep RL and Autonomous Practicing
We present a system that enables an autonomous small-scale RC car to drive aggressively from visual observations using reinforcement learning (RL). Our system, FastRLAP, trains autonomously in the real world, without human interven... | [
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corl_2023_FefFLN5FvIM | FefFLN5FvIM | corl | 2,023 | TactileVAD: Geometric Aliasing-Aware Dynamics for High-Resolution Tactile Control | Touch-based control is a promising approach to dexterous manipulation. However, existing tactile control methods often overlook tactile geometric aliasing which can compromise control performance and reliability. This type of aliasing occurs when different contact locations yield similar tactile signatures. To address ... | Miquel Oller;Dmitry Berenson;Nima Fazeli | University of Michigan - Ann Arbor;University of Michigan;University of Michigan | Poster | main | Manipulation;tactile control;high-resolution tactile sensors | https://openreview.net/forum?id=FefFLN5FvIM | 2 | TactileVAD: Geometric Aliasing-Aware Dynamics for High-Resolution Tactile Control
Touch-based control is a promising approach to dexterous manipulation. However, existing tactile control methods often overlook tactile geometric aliasing which can compromise control performance and reliability. This type of aliasing occ... | [
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corl_2023_Ffn8Z4Q-zU | Ffn8Z4Q-zU | corl | 2,023 | Gesture-Informed Robot Assistance via Foundation Models | Gestures serve as a fundamental and significant mode of non-verbal communication among humans. Deictic gestures (such as pointing towards an object), in particular, offer valuable means of efficiently expressing intent in situations where language is inaccessible, restricted, or highly specialized. As a result, it is e... | Li-Heng Lin;Yuchen Cui;Yilun Hao;Fei Xia;Dorsa Sadigh | Stanford University;Stanford University;Stanford University;Google;Stanford University | Poster | main | Planning with Gestures;Human-Robot Interaction;LLM Reasoning | https://openreview.net/forum?id=Ffn8Z4Q-zU | 20 | Gesture-Informed Robot Assistance via Foundation Models
Gestures serve as a fundamental and significant mode of non-verbal communication among humans. Deictic gestures (such as pointing towards an object), in particular, offer valuable means of efficiently expressing intent in situations where language is inaccessible,... | [
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corl_2023_G_FEL3OkiR | G_FEL3OkiR | corl | 2,023 | Human-in-the-Loop Task and Motion Planning for Imitation Learning | Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks, but they are difficult to apply to contact-rich tasks. In this paper, we prese... | Ajay Mandlekar;Caelan Reed Garrett;Danfei Xu;Dieter Fox | NVIDIA;NVIDIA;NVIDIA;Department of Computer Science | Poster | main | Imitation Learning;Task and Motion Planning;Teleoperation | https://openreview.net/forum?id=G_FEL3OkiR | 21 | Human-in-the-Loop Task and Motion Planning for Imitation Learning
Imitation learning from human demonstrations can teach robots complex manipulation skills, but is time-consuming and labor intensive. In contrast, Task and Motion Planning (TAMP) systems are automated and excel at solving long-horizon tasks, but they are... | [
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corl_2023_GsM2qJTAg- | GsM2qJTAg- | corl | 2,023 | Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees | Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric models that can be used to infer nonlinear functions with time-varying noise. In robotics, they can be employed for learning from demonstration as motion primitives, i.e. as a model of the trajectories to be executed by the robot. HGPs provide v... | Edoardo Caldarelli;Antoine Chatalic;Adrià Colomé;Lorenzo Rosasco;Carme Torras | Universidad Politécnica de Cataluna;;Spanish National Research Council;;Institut de Robòtica i Informàtica Industrial, CSIC-UPC | Poster | main | Gaussian process regression;random features;motion primitives | https://github.com/LCSL/rff-hgp | https://openreview.net/forum?id=GsM2qJTAg- | 0 | Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees
Heteroscedastic Gaussian processes (HGPs) are kernel-based, non-parametric models that can be used to infer nonlinear functions with time-varying noise. In robotics, they can be employed for learning from demonstration as... | [
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corl_2023_HCWoFkGe8L4 | HCWoFkGe8L4 | corl | 2,023 | Revisiting Depth-guided Methods for Monocular 3D Object Detection by Hierarchical Balanced Depth | Monocular 3D object detection has seen significant advancements with the incorporation of depth information. However, there remains a considerable performance gap compared to LiDAR-based methods, largely due to inaccurate depth estimation. We argue that this issue stems from the commonly used pixel-wise depth map loss,... | Yi-Rong Chen;Ching-Yu Tseng;Yi-Syuan Liou;Tsung-Han Wu;Winston H. Hsu | Department of computer science and informational engineering, National Taiwan University;National Taiwan University;National Taiwan University;National Taiwan University;National Taiwan University | Poster | main | monocular 3D object detection;autonomous driving | https://openreview.net/forum?id=HCWoFkGe8L4 | 1 | Revisiting Depth-guided Methods for Monocular 3D Object Detection by Hierarchical Balanced Depth
Monocular 3D object detection has seen significant advancements with the incorporation of depth information. However, there remains a considerable performance gap compared to LiDAR-based methods, largely due to inaccurate d... | [
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corl_2023_HDYMjiukjn | HDYMjiukjn | corl | 2,023 | RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning | Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has typically addressed corresponds to a rather narrow definition of dext... | Kevin Zakka;Philipp Wu;Laura Smith;Nimrod Gileadi;Taylor Howell;Xue Bin Peng;Sumeet Singh;Yuval Tassa;Pete Florence;Andy Zeng;Pieter Abbeel | University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;Google DeepMind;Stanford University;Simon Fraser University;Google Brain Robotics;Google;Covariant;Google;Google | Poster | main | high-dimensional control;bi-manual dexterity | https://github.com/google-research/robopianist | https://openreview.net/forum?id=HDYMjiukjn | 47 | RoboPianist: Dexterous Piano Playing with Deep Reinforcement Learning
Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has... | [
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corl_2023_HEIRj51lcS | HEIRj51lcS | corl | 2,023 | Polybot: Training One Policy Across Robots While Embracing Variability | Reusing large datasets is crucial to scale vision-based robotic manipulators to everyday scenarios due to the high cost of collecting robotic datasets. However, robotic platforms possess varying control schemes, camera viewpoints, kinematic configurations, and end-effector morphologies, posing significant challenges wh... | Jonathan Heewon Yang;Dorsa Sadigh;Chelsea Finn | Stanford University;Stanford University;Google | Poster | main | vision-based manipulation;multi-robot generalization | https://openreview.net/forum?id=HEIRj51lcS | 27 | Polybot: Training One Policy Across Robots While Embracing Variability
Reusing large datasets is crucial to scale vision-based robotic manipulators to everyday scenarios due to the high cost of collecting robotic datasets. However, robotic platforms possess varying control schemes, camera viewpoints, kinematic configur... | [
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corl_2023_HYka22IcV6 | HYka22IcV6 | corl | 2,023 | Online Learning for Obstacle Avoidance | We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations of uncertainty in the environment or a stationary stochastic model of uncertainty, we propose a method that is efficient to implement and p... | David Snyder;Meghan Booker;Nathaniel Simon;Wenhan Xia;Daniel Suo;Elad Hazan;Anirudha Majumdar | Princeton University;Princeton University;Princeton University;Princeton University;;Princeton University;Princeton University | Poster | main | Regret;Online;Learning;Convex;Optimization;Obstacle | https://openreview.net/forum?id=HYka22IcV6 | 3 | Online Learning for Obstacle Avoidance
We approach the fundamental problem of obstacle avoidance for robotic systems via the lens of online learning. In contrast to prior work that either assumes worst-case realizations of uncertainty in the environment or a stationary stochastic model of uncertainty, we propose a meth... | [
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corl_2023_HtJE9ly5dT | HtJE9ly5dT | corl | 2,023 | Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models | Long-horizon tasks, usually characterized by complex subtask dependencies, present a significant challenge in manipulation planning. Skill chaining is a practical approach to solving unseen tasks by combining learned skill priors. However, such methods are myopic if sequenced greedily and face scalability issues with s... | Utkarsh Aashu Mishra;Shangjie Xue;Yongxin Chen;Danfei Xu | Sony R&D US Labs;Georgia Institute of Technology;Georgia Institute of Technology;NVIDIA | Poster | main | Manipulation Planning;Diffusion Models;Task and Motion Planning | https://github.com/generative-skill-chaining/gsc-code | https://openreview.net/forum?id=HtJE9ly5dT | 79 | Generative Skill Chaining: Long-Horizon Skill Planning with Diffusion Models
Long-horizon tasks, usually characterized by complex subtask dependencies, present a significant challenge in manipulation planning. Skill chaining is a practical approach to solving unseen tasks by combining learned skill priors. However, suc... | [
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corl_2023_IM8zOC94HF | IM8zOC94HF | corl | 2,023 | Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching | Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinforcement Learning (RL) or Imitation Learning (IL), we require a more careful consideration of how uncertainty estimation interplays with firs... | H.J. Terry Suh;Glen Chou;Hongkai Dai;Lujie Yang;Abhishek Gupta;Russ Tedrake | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Toyota Research Institute;Massachusetts Institute of Technology;University of Washington;Massachusetts Institute of Technology | Poster | main | Model-Based Reinforcement Learning;Offline Reinforcement Learning;Planning under Uncertainty;Diffusion;Score Matching | https://github.com/hjsuh94/score_po | https://openreview.net/forum?id=IM8zOC94HF | 11 | Fighting Uncertainty with Gradients: Offline Reinforcement Learning via Diffusion Score Matching
Gradient-based methods enable efficient search capabilities in high dimensions. However, in order to apply them effectively in offline optimization paradigms such as offline Reinforcement Learning (RL) or Imitation Learning... | [
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corl_2023_Id4b5SY1Y8 | Id4b5SY1Y8 | corl | 2,023 | PairwiseNet: Pairwise Collision Distance Learning for High-dof Robot Systems | Motion planning for robot manipulation systems operating in complex environments remains a challenging problem. It requires the evaluation of both the collision distance and its derivative. Owing to its computational complexity, recent studies have attempted to utilize data-driven approaches to learn the collision dist... | Jihwan Kim;Frank C. Park | Seoul National University;Seoul National University | Poster | main | Robot Collision;Collision Distance;Machine Learning | https://github.com/kjh6526/PairwiseNet | https://openreview.net/forum?id=Id4b5SY1Y8 | 5 | PairwiseNet: Pairwise Collision Distance Learning for High-dof Robot Systems
Motion planning for robot manipulation systems operating in complex environments remains a challenging problem. It requires the evaluation of both the collision distance and its derivative. Owing to its computational complexity, recent studies... | [
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corl_2023_IeKC9khX5jD | IeKC9khX5jD | corl | 2,023 | Affordance-Driven Next-Best-View Planning for Robotic Grasping | Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a feasible grasp for target object via continuously observing scenes from new viewpoints. This... | Xuechao Zhang;Dong Wang;Sun Han;Weichuang Li;Bin Zhao;Zhigang Wang;Xiaoming Duan;Chongrong Fang;Xuelong Li;Jianping He | Shanghai Jiaotong University;Shanghai AI Laboratory;;Shanghai AI Laboratory;Northwest Polytechnical University Xi'an;Shanghai Jiaotong University;Shanghai Jiaotong University;Northwestern Polytechnical University;Shanghai Jiaotong University;Shanghai AI Lab | Poster | main | Grasp Synthesis;Neural SDF;Next-Best-View Planning | https://openreview.net/forum?id=IeKC9khX5jD | 14 | Affordance-Driven Next-Best-View Planning for Robotic Grasping
Grasping occluded objects in cluttered environments is an essential component in complex robotic manipulation tasks. In this paper, we introduce an AffordanCE-driven Next-Best-View planning policy (ACE-NBV) that tries to find a feasible grasp for target obj... | [
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corl_2023_JdpleC92J4 | JdpleC92J4 | corl | 2,023 | AR2-D2: Training a Robot Without a Robot | Diligently gathered human demonstrations serve as the unsung heroes empowering the progression of robot learning.
Today, demonstrations are collected by training people to use specialized controllers, which (tele-)operate robots to manipulate a small number of objects. By contrast, we introduce AR2-D2: a system for col... | Jiafei Duan;Yi Ru Wang;Mohit Shridhar;Dieter Fox;Ranjay Krishna | University of Washington;University of Washington;Department of Computer Science, University of Washington;Department of Computer Science;University of Washington | Poster | main | Demonstration collection;Imitation learning;Augmented reality;Manipulating personalized objects;Dataset collection;Behavior Cloning | https://github.com/jiafei1224/AR2-D2_Utils | https://openreview.net/forum?id=JdpleC92J4 | 30 | AR2-D2: Training a Robot Without a Robot
Diligently gathered human demonstrations serve as the unsung heroes empowering the progression of robot learning.
Today, demonstrations are collected by training people to use specialized controllers, which (tele-)operate robots to manipulate a small number of objects. By contra... | [
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corl_2023_JkFeyEC6VXV | JkFeyEC6VXV | corl | 2,023 | Finetuning Offline World Models in the Real World | Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to learn skills. Recently, offline RL has been proposed as a framework for t... | Yunhai Feng;Nicklas Hansen;Ziyan Xiong;Chandramouli Rajagopalan;Xiaolong Wang | ;University of California, San Diego;Tsinghua University;Cerenaut AI;University of California, San Diego | Oral | main | Model-Based Reinforcement Learning;Real-World Robotics | https://openreview.net/forum?id=JkFeyEC6VXV | 23 | Finetuning Offline World Models in the Real World
Reinforcement Learning (RL) is notoriously data-inefficient, which makes training on a real robot difficult. While model-based RL algorithms (world models) improve data-efficiency to some extent, they still require hours or days of interaction to learn skills. Recently,... | [
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corl_2023_K8cGp8rVLIP | K8cGp8rVLIP | corl | 2,023 | Neural Field Dynamics Model for Granular Object Piles Manipulation | We present a learning-based dynamics model for granular material manipulation. Drawing inspiration from computer graphics' Eulerian approach, our method adopts a fully convolutional neural network that operates on a density field-based representation of object piles, allowing it to exploit the spatial locality of inter... | Shangjie Xue;Shuo Cheng;Pujith Kachana;Danfei Xu | Georgia Institute of Technology;Georgia Institute of Technology;;NVIDIA | Poster | main | Deformable Object Manipulation;Manipulation Planning | https://sites.google.com/view/nfd-corl23/ | https://openreview.net/forum?id=K8cGp8rVLIP | 8 | Neural Field Dynamics Model for Granular Object Piles Manipulation
We present a learning-based dynamics model for granular material manipulation. Drawing inspiration from computer graphics' Eulerian approach, our method adopts a fully convolutional neural network that operates on a density field-based representation of... | [
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corl_2023_MF_cS7TCYk | MF_cS7TCYk | corl | 2,023 | Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors | Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn ... | Sushant Veer;Apoorva Sharma;Marco Pavone | NVIDIA;NVIDIA;Stanford University | Poster | main | trajectory prediction;rule-based planning | https://openreview.net/forum?id=MF_cS7TCYk | 6 | Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors
Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced conside... | [
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corl_2023_MnANx01rV2w | MnANx01rV2w | corl | 2,023 | CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller | We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-level centroidal policy and a low-level leg controller. In particular, we use reinforcement learning (RL) to train the centroidal policy, which ... | Yuxiang Yang;Guanya Shi;Xiangyun Meng;Wenhao Yu;Tingnan Zhang;Jie Tan;Byron Boots | Google;University of Washington;University of Washington;Google;Google;Google; | Poster | main | Jumping;Legged Locomotion;Reinforcement Learning | https://github.com/yxyang/cajun/ | https://openreview.net/forum?id=MnANx01rV2w | 30 | CAJun: Continuous Adaptive Jumping using a Learned Centroidal Controller
We present CAJun, a novel hierarchical learning and control framework that enables legged robots to jump continuously with adaptive jumping distances. CAJun consists of a high-level centroidal policy and a low-level leg controller. In particular, ... | [
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corl_2023_N3VbFUpwaa | N3VbFUpwaa | corl | 2,023 | Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities | Recent advances in multi-agent reinforcement learning (MARL) are enabling impressive coordination in heterogeneous multi-robot teams. However, existing approaches often overlook the challenge of generalizing learned policies to teams of new compositions, sizes, and robots. While such generalization might not be importa... | Pierce Howell;Max Rudolph;Reza Joseph Torbati;Kevin Fu;Harish Ravichandar | Georgia Institute of Technology;University of Texas at Austin;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology | Poster | main | Heterogeneity;Multi-Robot Teaming;Generalization | https://openreview.net/forum?id=N3VbFUpwaa | 6 | Generalization of Heterogeneous Multi-Robot Policies via Awareness and Communication of Capabilities
Recent advances in multi-agent reinforcement learning (MARL) are enabling impressive coordination in heterogeneous multi-robot teams. However, existing approaches often overlook the challenge of generalizing learned pol... | [
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corl_2023_Nii0_rRJwN | Nii0_rRJwN | corl | 2,023 | CALAMARI: Contact-Aware and Language conditioned spatial Action MApping for contact-RIch manipulation | Making contact with purpose is a central part of robot manipulation and remains essential for many household tasks -- from sweeping dust into a dustpan, to wiping tables; from erasing whiteboards, to applying paint. In this work, we investigate learning language-conditioned, vision-based manipulation policies wherein t... | Youngsun Wi;Mark Van der Merwe;Pete Florence;Andy Zeng;Nima Fazeli | University of Michigan;University of Michigan - Ann Arbor;Google;University of Michigan;Google | Poster | main | Contact-rich Manipulation;Visual-language guided policies | https://openreview.net/forum?id=Nii0_rRJwN | 3 | CALAMARI: Contact-Aware and Language conditioned spatial Action MApping for contact-RIch manipulation
Making contact with purpose is a central part of robot manipulation and remains essential for many household tasks -- from sweeping dust into a dustpan, to wiping tables; from erasing whiteboards, to applying paint. In... | [
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corl_2023_PK2debCKaG | PK2debCKaG | corl | 2,023 | Language Conditioned Traffic Generation | Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in ren... | Shuhan Tan;Boris Ivanovic;Xinshuo Weng;Marco Pavone;Philipp Kraehenbuehl | NVIDIA;NVIDIA;NVIDIA;Stanford University;Apple | Poster | main | Self-driving;Content generation;Large language model | https://github.com/Ariostgx/lctgen | https://openreview.net/forum?id=PK2debCKaG | 63 | Language Conditioned Traffic Generation
Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesti... | [
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corl_2023_PalhNjBJqv | PalhNjBJqv | corl | 2,023 | A Data-efficient Neural ODE Framework for Optimal Control of Soft Manipulators | This paper introduces a novel approach for modeling continuous forward kinematic models of soft continuum robots by employing Augmented Neural ODE (ANODE), a cutting-edge family of deep neural network models. To the best of our knowledge, this is the first application of ANODE in modeling soft continuum robots. This fo... | Mohammadreza Kasaei;Keyhan Kouhkiloui Babarahmati;Zhibin Li;Mohsen Khadem | University of Edinburgh, University of Edinburgh;University of Edinburgh, University of Edinburgh;University College London, University of London;Edinburgh University, University of Edinburgh | Poster | main | Soft robots;Non-parametric modelling;Optimal control | https://github.com/MohammadKasaei/SoftRobotSimulator | https://openreview.net/forum?id=PalhNjBJqv | 5 | A Data-efficient Neural ODE Framework for Optimal Control of Soft Manipulators
This paper introduces a novel approach for modeling continuous forward kinematic models of soft continuum robots by employing Augmented Neural ODE (ANODE), a cutting-edge family of deep neural network models. To the best of our knowledge, th... | [
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corl_2023_PsV65r0itpo | PsV65r0itpo | corl | 2,023 | Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning | Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar settings with such methods often requires lengthy mapping and exploration. Humans can rapid... | Dhruv Shah;Michael Robert Equi;Błażej Osiński;Fei Xia;brian ichter;Sergey Levine | UC Berkeley;University of California, Berkeley;University of Warsaw;Google;Google;Google | Poster | main | navigation;language models;planning;semantics | https://github.com/Michael-Equi/lfg-nav | https://openreview.net/forum?id=PsV65r0itpo | 118 | Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning
Navigation in unfamiliar environments presents a major challenge for robots: while mapping and planning techniques can be used to build up a representation of the world, quickly discovering a path to a desired goal in unfamiliar setti... | [
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corl_2023_PwqiqaaEzJ | PwqiqaaEzJ | corl | 2,023 | MUTEX: Learning Unified Policies from Multimodal Task Specifications | Humans use different modalities, such as speech, text, images, videos, etc., to communicate their intent and goals with teammates. For robots to become better assistants, we aim to endow them with the ability to follow instructions and understand tasks specified by their human partners. Most robotic policy learning met... | Rutav Shah;Roberto Martín-Martín;Yuke Zhu | University of Texas at Austin;University of Texas at Austin;Computer Science Department, University of Texas, Austin | Poster | main | Multimodal Learning;Task Specification;Manipulation | https://github.com/UT-Austin-RPL/MUTEX | https://openreview.net/forum?id=PwqiqaaEzJ | 65 | MUTEX: Learning Unified Policies from Multimodal Task Specifications
Humans use different modalities, such as speech, text, images, videos, etc., to communicate their intent and goals with teammates. For robots to become better assistants, we aim to endow them with the ability to follow instructions and understand task... | [
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corl_2023_Pwsm7d0iWJD | Pwsm7d0iWJD | corl | 2,023 | Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation | Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrated trajectories. Stability guarantees have previously been provided util... | Amin Abyaneh;Hsiu-Chin Lin | McGill University;McGill University | Poster | main | Imitation learning;Safe learning;Motion planning;Dynamical system;Semidefinite programming;Robotic manipulation | https://github.com/aminabyaneh/stable-imitation-policy | https://openreview.net/forum?id=Pwsm7d0iWJD | 4 | Learning Lyapunov-Stable Polynomial Dynamical Systems Through Imitation
Imitation learning is a paradigm to address complex motion planning problems by learning a policy to imitate an expert's behavior. However, relying solely on the expert's data might lead to unsafe actions when the robot deviates from the demonstrat... | [
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corl_2023_Q8BGLiWn2X | Q8BGLiWn2X | corl | 2,023 | PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining | A rich representation is key to general robotic manipulation, but existing approaches to representation learning require large amounts of multimodal demonstrations. In this work we propose PLEX, a transformer-based architecture that learns from a small amount of task-agnostic visuomotor trajectories and a much larger a... | Garrett Thomas;Ching-An Cheng;Ricky Loynd;Felipe Vieira Frujeri;Vibhav Vineet;Mihai Jalobeanu;Andrey Kolobov | Stanford University;Microsoft Research;Microsoft Research;;;Microsoft Research;Microsoft | Poster | main | Robot learning;Robotic manipulation;Visuomotor representations | https://openreview.net/forum?id=Q8BGLiWn2X | 12 | PLEX: Making the Most of the Available Data for Robotic Manipulation Pretraining
A rich representation is key to general robotic manipulation, but existing approaches to representation learning require large amounts of multimodal demonstrations. In this work we propose PLEX, a transformer-based architecture that learns... | [
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corl_2023_Q9ezhChqnL | Q9ezhChqnL | corl | 2,023 | Towards Scalable Coverage-Based Testing of Autonomous Vehicles | To deploy autonomous vehicles(AVs) in the real world, developers must understand the conditions in which the system can operate safely. To do this in a scalable manner, AVs are often tested in simulation on parameterized scenarios. In this context, it’s important to build a testing framework that partitions the scenari... | James Tu;Simon Suo;Chris Zhang;Kelvin Wong;Raquel Urtasun | Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto;Department of Computer Science, University of Toronto | Poster | main | Self-Driving;Coverage;Testing | https://openreview.net/forum?id=Q9ezhChqnL | 4 | Towards Scalable Coverage-Based Testing of Autonomous Vehicles
To deploy autonomous vehicles(AVs) in the real world, developers must understand the conditions in which the system can operate safely. To do this in a scalable manner, AVs are often tested in simulation on parameterized scenarios. In this context, it’s imp... | [
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corl_2023_QG_ERxtDAP- | QG_ERxtDAP- | corl | 2,023 | Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks | Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper proposes an intrinsically motivated RL approach to reduce task-specifi... | Clemens Schwarke;Victor Klemm;Matthijs van der Boon;Marko Bjelonic;Marco Hutter | ETHZ - ETH Zurich;ETHZ - ETH Zurich;;;ETHZ - ETH Zurich | Poster | main | Curiosity;Reinforcement Learning;Wheeled-Legged Robots | https://openreview.net/forum?id=QG_ERxtDAP- | 19 | Curiosity-Driven Learning of Joint Locomotion and Manipulation Tasks
Learning complex locomotion and manipulation tasks presents significant challenges, often requiring extensive engineering of, e.g., reward functions or curricula to provide meaningful feedback to the Reinforcement Learning (RL) algorithm. This paper p... | [
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corl_2023_QNPuJZyhFE | QNPuJZyhFE | corl | 2,023 | Imitating Task and Motion Planning with Visuomotor Transformers | Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale poorly, as they are time-consuming and labor-intensive. In contras... | Murtaza Dalal;Ajay Mandlekar;Caelan Reed Garrett;Ankur Handa;Ruslan Salakhutdinov;Dieter Fox | Carnegie Mellon University;NVIDIA;NVIDIA;Imperial College London;Department of Computer Science;School of Computer Science, Carnegie Mellon University | Poster | main | Imitation Learning;Task and Motion Planning;Transformers | https://openreview.net/forum?id=QNPuJZyhFE | 56 | Imitating Task and Motion Planning with Visuomotor Transformers
Imitation learning is a powerful tool for training robot manipulation policies, allowing them to learn from expert demonstrations without manual programming or trial-and-error. However, common methods of data collection, such as human supervision, scale po... | [
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... | ||
corl_2023_RN00jfIV-X | RN00jfIV-X | corl | 2,023 | General In-hand Object Rotation with Vision and Touch | We introduce Rotateit, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuota... | Haozhi Qi;Brent Yi;Sudharshan Suresh;Mike Lambeta;Yi Ma;Roberto Calandra;Jitendra Malik | University of California, Berkeley;University of California, Berkeley;Carnegie Mellon University;Meta;University of California, Berkeley;Meta Facebook;University of California, Berkeley | Poster | main | In-Hand Object Rotation;Tactile Sensing;Reinforcement Learning;Sim2Real;Transformer;Visuotactile Manipulation | https://openreview.net/forum?id=RN00jfIV-X | 106 | General In-hand Object Rotation with Vision and Touch
We introduce Rotateit, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill ... | [
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... | ||
corl_2023_RQ_7yVV8vA | RQ_7yVV8vA | corl | 2,023 | Learning to See Physical Properties with Active Sensing Motor Policies | To plan efficient robot locomotion, we must use the information about a terrain’s physics that can be inferred from color images. To this end, we train a visual perception module that predicts terrain properties using labels from a small amount of real-world proprioceptive locomotion. To ensure label precision, we intr... | Gabriel B. Margolis;Xiang Fu;Yandong Ji;Pulkit Agrawal | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | Locomotion;Vision;Navigation;Reinforcement Learning | https://openreview.net/forum?id=RQ_7yVV8vA | 16 | Learning to See Physical Properties with Active Sensing Motor Policies
To plan efficient robot locomotion, we must use the information about a terrain’s physics that can be inferred from color images. To this end, we train a visual perception module that predicts terrain properties using labels from a small amount of r... | [
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corl_2023_RaNAaxZfKi8 | RaNAaxZfKi8 | corl | 2,023 | One-shot Imitation Learning via Interaction Warping | Learning robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for one-shot learning SE(3) robotic manipulation policies. We infer the 3D mesh of each object in the environment using shape warping, a technique for aligning point clouds across object ... | Ondrej Biza;Skye Thompson;Kishore Reddy Pagidi;Abhinav Kumar;Elise van der Pol;Robin Walters;Thomas Kipf;Jan-Willem van de Meent;Lawson L.S. Wong;Robert Platt | Northeastern University;Brown University;Northeastern University;Northeastern university ;Microsoft Research;Northeastern University ;Northeastern University;Northeastern University;Northeastern University;Google | Poster | main | 3D manipulation;imitation learning;shape warping | https://github.com/ondrejbiza/shapewarping | https://openreview.net/forum?id=RaNAaxZfKi8 | 13 | One-shot Imitation Learning via Interaction Warping
Learning robot policies from few demonstrations is crucial in open-ended applications. We propose a new method, Interaction Warping, for one-shot learning SE(3) robotic manipulation policies. We infer the 3D mesh of each object in the environment using shape warping, ... | [
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corl_2023_Rb0nGIt_kh5 | Rb0nGIt_kh5 | corl | 2,023 | Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation | Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. This work bridges this 2D-to-3D gap for robotic manipulation by levera... | William Shen;Ge Yang;Alan Yu;Jansen Wong;Leslie Pack Kaelbling;Phillip Isola | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Oral | main | Neural Fields;Foundation Models;Scene Understanding;Robot Manipulation | https://github.com/f3rm/f3rm | https://openreview.net/forum?id=Rb0nGIt_kh5 | 113 | Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation
Self-supervised and language-supervised image models contain rich knowledge of the world that is important for generalization. Many robotic tasks, however, require a detailed understanding of 3D geometry, which is often lacking in 2D image features. ... | [
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corl_2023_RcZMI8MSyE | RcZMI8MSyE | corl | 2,023 | Large Language Models as General Pattern Machines | We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences—from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstraction and Reasoning Corpus (ARC), a general AI benchmark,... | Suvir Mirchandani;Fei Xia;Pete Florence;brian ichter;Danny Driess;Montserrat Gonzalez Arenas;Kanishka Rao;Dorsa Sadigh;Andy Zeng | Google;Google;Google;Google;Technische Universität Berlin;;;Stanford University;Google | Poster | main | large language models;in-context learning;language for robotics | https://openreview.net/forum?id=RcZMI8MSyE | 220 | Large Language Models as General Pattern Machines
We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences—from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstraction a... | [
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corl_2023_SgTPdyehXMA | SgTPdyehXMA | corl | 2,023 | Language to Rewards for Robotic Skill Synthesis | Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic control. However, since low-level robot actions ar... | Wenhao Yu;Nimrod Gileadi;Chuyuan Fu;Sean Kirmani;Kuang-Huei Lee;Montserrat Gonzalez Arenas;Hao-Tien Lewis Chiang;Tom Erez;Leonard Hasenclever;Jan Humplik;brian ichter;Ted Xiao;Peng Xu;Andy Zeng;Tingnan Zhang;Nicolas Heess;Dorsa Sadigh;Jie Tan;Yuval Tassa;Fei Xia | Google;Google DeepMind;Google;Google X;Google;;Google Deepmind;;Google DeepMind;Google DeepMind;Google;;Google;Google;Google DeepMind;Stanford University;Google;Google;Google;Google | Oral | main | Large language model (LLM);Low-level skill learning;Legged locomotion;Dexterous manipulation | https://openreview.net/forum?id=SgTPdyehXMA | 326 | Language to Rewards for Robotic Skill Synthesis
Large language models (LLMs) have demonstrated exciting progress in acquiring diverse new capabilities through in-context learning, ranging from logical reasoning to code-writing. Robotics researchers have also explored using LLMs to advance the capabilities of robotic co... | [
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corl_2023_TWgoGdubPN | TWgoGdubPN | corl | 2,023 | Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models | We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data.
In recent years, policy gradient methods have emerged as a promising paradigm for training control policies in simulation.
However, these approaches often remain too data inefficient or unreliable to ... | Tyler Westenbroek;Jacob Levy;David Fridovich-Keil | ;University of Texas at Austin;University of Texas at Austin | Poster | main | Model-based Reinforcement Learning;Feedback Control;Quadrupedal Locomotion | https://github.com/CLeARoboticsLab/LearningWithSimpleModels.jl | https://openreview.net/forum?id=TWgoGdubPN | 3 | Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data.
In recent years, policy gradient methods have emerged as a promising paradigm for training control p... | [
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corl_2023_TgJ8vJUVUBR | TgJ8vJUVUBR | corl | 2,023 | TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning | Multi-agent reinforcement learning (MARL) has emerged as a popular technique in diverse domains due to its ability to automate system controller design and facilitate continuous intelligence learning. For instance, traffic flow is often trained with MARL to enable intelligent simulations for autonomous driving. However... | Weiwei Liu;Wei Jing;lingping Gao;Ke Guo;Gang Xu;Yong Liu | Zhejiang University;Alibaba Group;;University of Hong Kong;;Zhejiang University | Poster | main | autonomous driving;multi-agent reinforcement learning;counterfactual reasoning | https://openreview.net/forum?id=TgJ8vJUVUBR | 2 | TraCo: Learning Virtual Traffic Coordinator for Cooperation with Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) has emerged as a popular technique in diverse domains due to its ability to automate system controller design and facilitate continuous intelligence learning. For instance, traff... | [
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corl_2023_Tka2U40pHz0 | Tka2U40pHz0 | corl | 2,023 | Tuning Legged Locomotion Controllers via Safe Bayesian Optimization | This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the mismatch between the simplified model used in the control formulati... | Daniel Widmer;Dongho Kang;Bhavya Sukhija;Jonas Hübotter;Andreas Krause;Stelian Coros | ETHZ - ETH Zurich;ETHZ - ETH Zurich;ETHZ - ETH Zurich;ETH Zurich;ETH Zurich;ETHZ - ETH Zurich | Poster | main | Legged Robotics;Bayesian Optimization;Controller Tuning;Locomotion;Machine Learning;Safe Learning | https://github.com/lasgroup/gosafeopt | https://openreview.net/forum?id=Tka2U40pHz0 | 21 | Tuning Legged Locomotion Controllers via Safe Bayesian Optimization
This paper presents a data-driven strategy to streamline the deployment of model-based controllers in legged robotic hardware platforms. Our approach leverages a model-free safe learning algorithm to automate the tuning of control gains, addressing the... | [
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corl_2023_UVARkqnsDd | UVARkqnsDd | corl | 2,023 | ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction | We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with r... | Jingyi Yu;Zizhao Zhang;Shengfu Xia;Jizhang Sang | Wuhan University;Wuhan University;Wuhan University;Wuhan University | Poster | main | Map Construction;Multi-view Perception;Long-range Perception | https://github.com/jingy1yu/ScalableMap | https://openreview.net/forum?id=UVARkqnsDd | 18 | ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction
We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map... | [
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0.05302191153168678... | |
corl_2023_UZpWSDA3tZJ | UZpWSDA3tZJ | corl | 2,023 | Towards General Single-Utensil Food Acquisition with Human-Informed Actions | Food acquisition with common general-purpose utensils is a necessary component of robot applications like in-home assistive feeding. Learning acquisition policies in this space is difficult in part because any model will need to contend with extensive state and actions spaces. Food is extremely diverse and generally di... | Ethan Kroll Gordon;Amal Nanavati;Ramya Challa;Bernie Hao Zhu;Taylor Annette Kessler Faulkner;Siddhartha Srinivasa | Department of Computer Science, University of Washington;Department of Computer Science;Oregon State University;;;University of Washington | Poster | main | Manipulation;Learning from Demonstration;Assistive Robotics | https://github.com/personalrobotics/corl23_towards_general_food_acquisition (implementation: https://github.com/personalrobotics/ada_feeding) | https://openreview.net/forum?id=UZpWSDA3tZJ | 11 | Towards General Single-Utensil Food Acquisition with Human-Informed Actions
Food acquisition with common general-purpose utensils is a necessary component of robot applications like in-home assistive feeding. Learning acquisition policies in this space is difficult in part because any model will need to contend with ex... | [
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corl_2023_VH6WIPF4Sj | VH6WIPF4Sj | corl | 2,023 | Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks | Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out by human workers as stowing is challenging to automate due to the complex multi-object interactions and long-horizon nature of the task. Pr... | Haonan Chen;Yilong Niu;Kaiwen Hong;Shuijing Liu;Yixuan Wang;Yunzhu Li;Katherine Rose Driggs-Campbell | University of Illinois Urbana-Champaign;University of Illinois Urbana-Champaign;UIUC;University of Illinois, Urbana Champaign;University of Illinois, Urbana Champaign;Stanford University; | Oral | main | Robotic Manipulation;Model Learning;Graph-Based Neural Dynamics;Multi-Object Interactions | https://openreview.net/forum?id=VH6WIPF4Sj | 11 | Predicting Object Interactions with Behavior Primitives: An Application in Stowing Tasks
Stowing, the task of placing objects in cluttered shelves or bins, is a common task in warehouse and manufacturing operations. However, this task is still predominantly carried out by human workers as stowing is challenging to auto... | [
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... |
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