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corl_2024_jPkOFAiOzf | jPkOFAiOzf | corl | 2,024 | Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping | A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware... | Siang Chen;Pengwei Xie;Wei Tang;Dingchang Hu;Yixiang Dai;Guijin Wang | Tsinghua University;;;;;Department of Electronic Engineering, Tsinghua University | Poster | main | 6-DoF Grasping;RGBD Perception;Normalized Space;Heatmap | https://github.com/THU-VCLab/RegionNormalizedGrasp | https://openreview.net/forum?id=jPkOFAiOzf | 1 | Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping
A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsis... | [
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corl_2024_jart4nhCQr | jart4nhCQr | corl | 2,024 | Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning | Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose Maniwhere, a generalizable framework tailored for visual reinforcement learning, enabling the trained robot policies to generalize across a combination of multiple visual disturbance typ... | Zhecheng Yuan;Tianming Wei;Shuiqi Cheng;Gu Zhang;Yuanpei Chen;Huazhe Xu | ;Shanghai Jiaotong University;University of Hong Kong;Shanghai Jiaotong University;PsiRobot;Tsinghua University | Poster | main | Visual Generalization;Sim2real;Reinforcement Learning | https://openreview.net/forum?id=jart4nhCQr | 22 | Learning to Manipulate Anywhere: A Visual Generalizable Framework For Reinforcement Learning
Can we endow visuomotor robots with generalization capabilities to operate in diverse open-world scenarios? In this paper, we propose Maniwhere, a generalizable framework tailored for visual reinforcement learning, enabling the... | [
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corl_2024_jnubz7wB2w | jnubz7wB2w | corl | 2,024 | Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation | Control barrier functions (CBFs) are important in safety-critical systems and robot control applications. Neural networks have been used to parameterize and synthesize CBFs with bounded control input for complex systems. However, it is still challenging to verify pre-trained neural networks CBFs (neural CBFs) in an eff... | Hanjiang Hu;Yujie Yang;Tianhao Wei;Changliu Liu | School of Computer Science, Carnegie Mellon University;Tsinghua University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Learning for control;control barrier function;formal verification | https://github.com/intelligent-control-lab/verify-neural-CBF | https://openreview.net/forum?id=jnubz7wB2w | 8 | Verification of Neural Control Barrier Functions with Symbolic Derivative Bounds Propagation
Control barrier functions (CBFs) are important in safety-critical systems and robot control applications. Neural networks have been used to parameterize and synthesize CBFs with bounded control input for complex systems. Howeve... | [
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corl_2024_k0ogr4dnhG | k0ogr4dnhG | corl | 2,024 | ClutterGen: A Cluttered Scene Generator for Robot Learning | We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and collision. As the number of objects increases, finding valid... | Yinsen Jia;Boyuan Chen | Duke University;Duke University | Poster | main | Simulation Scene Generation;Manipulation;Robot Learning | https://github.com/generalroboticslab/ClutterGen | https://openreview.net/forum?id=k0ogr4dnhG | 4 | ClutterGen: A Cluttered Scene Generator for Robot Learning
We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and c... | [
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corl_2024_k4Nnxqcwt8 | k4Nnxqcwt8 | corl | 2,024 | Q-SLAM: Quadric Representations for Monocular SLAM | In this paper, we reimagine volumetric representations through the lens of quadrics. We posit that rigid scene components can be effectively decomposed into quadric surfaces. Leveraging this assumption, we reshape the volumetric representations with million of cubes by several quadric planes, which results in more accu... | Chensheng Peng;Chenfeng Xu;Yue Wang;Mingyu Ding;Heng Yang;Masayoshi Tomizuka;Kurt Keutzer;Marco Pavone;Wei Zhan | ;University of California, Berkeley;NVIDIA;University of California, Berkeley;NVIDIA;;University of California, Berkeley;Stanford University; | Poster | main | Neural Radiance Fields;Simultaneous Localization and Mapping | https://github.com/PholyPeng/Q-SLAM | https://openreview.net/forum?id=k4Nnxqcwt8 | 6 | Q-SLAM: Quadric Representations for Monocular SLAM
In this paper, we reimagine volumetric representations through the lens of quadrics. We posit that rigid scene components can be effectively decomposed into quadric surfaces. Leveraging this assumption, we reshape the volumetric representations with million of cubes by... | [
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corl_2024_kEZXeaMrkD | kEZXeaMrkD | corl | 2,024 | Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance | In this work, we address the challenging problem of long-horizon goal-reaching policy learning from non-expert, action-free observation data. Unlike fully labeled expert data, our data is more accessible and avoids the costly process of action labeling. Additionally, compared to online learning, which often involves ai... | RenMing Huang;Shaochong Liu;Yunqiang Pei;Peng Wang;Guoqing Wang;Yang Yang;Heng Tao Shen | University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science and Technology of China;University of Electronic Science an... | Poster | main | Goal-Reaching;Long-Horizon;Non-Expert Observation Data | https://github.com/RenMing-Huang/EGR-PO | https://openreview.net/forum?id=kEZXeaMrkD | 1 | Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance
In this work, we address the challenging problem of long-horizon goal-reaching policy learning from non-expert, action-free observation data. Unlike fully labeled expert data, our data is more accessible and avoids the costly proc... | [
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corl_2024_lKGRPJFPCM | lKGRPJFPCM | corl | 2,024 | InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation | We present InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers, a novel imitation learning framework for bimanual manipulation that integrates hierarchical attention to capture inter-dependencies between dual-arm joint states and visual inputs. InterACT consists of a Hierarchical A... | Andrew Choong-Won Lee;Ian Chuang;Ling-Yuan Chen;Iman Soltani | University of California, Davis;University of California, Davis;University of California, Davis;University of California, Davis | Poster | main | Robotics;Imitation Learning;Bimanual Manipulation | https://openreview.net/forum?id=lKGRPJFPCM | 7 | InterACT: Inter-dependency Aware Action Chunking with Hierarchical Attention Transformers for Bimanual Manipulation
We present InterACT: Inter-dependency aware Action Chunking with Hierarchical Attention Transformers, a novel imitation learning framework for bimanual manipulation that integrates hierarchical attention ... | [
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corl_2024_lpjPft4RQT | lpjPft4RQT | corl | 2,024 | TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction | Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require domain-specific knowledge *a priori*. We argue that a straightforward way ... | Yunfan Jiang;Chen Wang;Ruohan Zhang;Jiajun Wu;Li Fei-Fei | Stanford University;Computer Science Department, Stanford University;Stanford University;Stanford University;Stanford University | Poster | main | Sim-to-Real Transfer;Human-in-the-Loop;Robot Manipulation | https://github.com/transic-robot/transic | https://openreview.net/forum?id=lpjPft4RQT | 28 | TRANSIC: Sim-to-Real Policy Transfer by Learning from Online Correction
Learning in simulation and transferring the learned policy to the real world has the potential to enable generalist robots. The key challenge of this approach is to address simulation-to-reality (sim-to-real) gaps. Previous methods often require do... | [
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corl_2024_lt0Yf8Wh5O | lt0Yf8Wh5O | corl | 2,024 | Differentiable Robot Rendering | Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them to robotic tasks is the modality gap between visual data and action data. We introduce differentiable robot rendering, a method allowing the ... | Ruoshi Liu;Alper Canberk;Shuran Song;Carl Vondrick | Columbia University;Columbia University;Stanford University;Columbia University | Poster | main | Robot Representation;Visual Foundation Model | https://github.com/cvlab-columbia/drrobot | https://openreview.net/forum?id=lt0Yf8Wh5O | 6 | Differentiable Robot Rendering
Vision foundation models trained on massive amounts of visual data have shown unprecedented reasoning and planning skills in open-world settings. A key challenge in applying them to robotic tasks is the modality gap between visual data and action data. We introduce differentiable robot re... | [
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corl_2024_lyhS75loxe | lyhS75loxe | corl | 2,024 | A3VLM: Actionable Articulation-Aware Vision Language Model | Vision Language Models (VLMs) for robotics have received significant attention in recent years. As a VLM can understand robot observations and perform complex visual reasoning, it is regarded as a potential universal solution for general robotics challenges such as manipulation and navigation. However, previous robotic... | Siyuan Huang;Haonan Chang;Yuhan Liu;Yimeng Zhu;Hao Dong;Abdeslam Boularias;Peng Gao;Hongsheng Li | Shanghai Jiaotong University;Rutgers, New Brunswick;Rutgers University;Yuandao AI;Peking University;, Rutgers University;The Chinese University of Hong Kong;shanghai ai lab | Poster | main | LLM;VLM;Manipulation;Articulation | https://github.com/changhaonan/A3VLM | https://openreview.net/forum?id=lyhS75loxe | 12 | A3VLM: Actionable Articulation-Aware Vision Language Model
Vision Language Models (VLMs) for robotics have received significant attention in recent years. As a VLM can understand robot observations and perform complex visual reasoning, it is regarded as a potential universal solution for general robotics challenges suc... | [
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corl_2024_ma7McOiCZY | ma7McOiCZY | corl | 2,024 | HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation | Enabling robots to autonomously perform hybrid motions in diverse environments can be beneficial for long-horizon tasks such as material handling, household chores, and work assistance. This requires extensive exploitation of intrinsic motion capabilities, extraction of affordances from rich environmental information, ... | Jin Wang;Rui Dai;Weijie Wang;Luca Rossini;Francesco Ruscelli;Nikos Tsagarakis | Istituto Italiano di Tecnologia;Università degli Studi di Genova, Istituto Italiano di Tecnologia;Università degli Studi di Genova, Istituto Italiano di Tecnologia;;; | Poster | main | Loco-manipulation;Large Language Models;Humanoid Robot Learning | https://openreview.net/forum?id=ma7McOiCZY | 6 | HYPERmotion: Learning Hybrid Behavior Planning for Autonomous Loco-manipulation
Enabling robots to autonomously perform hybrid motions in diverse environments can be beneficial for long-horizon tasks such as material handling, household chores, and work assistance. This requires extensive exploitation of intrinsic moti... | [
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corl_2024_nQslM6f7dW | nQslM6f7dW | corl | 2,024 | APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs | Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances.
We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for placement collide with physical limitations.
The robot must infer user preferences ... | Huaxiaoyue Wang;Nathaniel Chin;Gonzalo Gonzalez-Pumariega;Xiangwan Sun;Neha Sunkara;Maximus Adrian Pace;Jeannette Bohg;Sanjiban Choudhury | Cornell University;Cornell University;Cornell University;Cornell University;Cornell University;Cornell University;Stanford University;Cornell University | Poster | main | Active Preference Learning;Task Planning;Large Language Models | https://github.com/portal-cornell/apricot | https://openreview.net/forum?id=nQslM6f7dW | 3 | APRICOT: Active Preference Learning and Constraint-Aware Task Planning with LLMs
Home robots performing personalized tasks must adeptly balance user preferences with environmental affordances.
We focus on organization tasks within constrained spaces, such as arranging items into a refrigerator, where preferences for pl... | [
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corl_2024_nVJm2RdPDu | nVJm2RdPDu | corl | 2,024 | DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets | Offline learning at scale has led to breakthroughs in computer vision, natural language processing, and robotic manipulation domains. However, scaling up learning for legged robot locomotion, especially with multiple skills in a single policy, presents significant challenges for prior online reinforcement learning (RL)... | Xiaoyu Huang;Yufeng Chi;Ruofeng Wang;Zhongyu Li;Xue Bin Peng;Sophia Shao;Borivoje Nikolic;Koushil Sreenath | University of California, Berkeley;;University of California, Berkeley;University of California, Berkeley;Simon Fraser University;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley | Poster | main | Offline Learning;Bipedal Walking;Imitation Learning | https://github.com/HybridRobotics/DiffuseLoco | https://openreview.net/forum?id=nVJm2RdPDu | 29 | DiffuseLoco: Real-Time Legged Locomotion Control with Diffusion from Offline Datasets
Offline learning at scale has led to breakthroughs in computer vision, natural language processing, and robotic manipulation domains. However, scaling up learning for legged robot locomotion, especially with multiple skills in a singl... | [
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corl_2024_nmEt0ci8hi | nmEt0ci8hi | corl | 2,024 | General Flow as Foundation Affordance for Scalable Robot Learning | We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and universal learning.
Therefore, we propose to utilize 3D flow, which repre... | Chengbo Yuan;Chuan Wen;Tong Zhang;Yang Gao | University of California, Berkeley;Tsinghua University;Tsinghua University;Wuhan University | Poster | main | Flow;Transferable Affordance;Scalability | https://github.com/michaelyuancb/general_flow | https://openreview.net/forum?id=nmEt0ci8hi | 41 | General Flow as Foundation Affordance for Scalable Robot Learning
We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and univers... | [
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corl_2024_oL1WEZQal8 | oL1WEZQal8 | corl | 2,024 | OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning | We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a full-sized humanoid with dexterous hands, including using real-time teleoperation through ... | Tairan He;Zhengyi Luo;Xialin He;Wenli Xiao;Chong Zhang;Weinan Zhang;Kris M. Kitani;Changliu Liu;Guanya Shi | Carnegie Mellon University;Meta Platforms, Inc.;Shanghai Jiaotong University;Carnegie Mellon University;ETHZ - ETH Zurich;Shanghai Jiaotong University;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Humanoid Teleoperation;Humanoid Loco-Manipulation;RL | https://github.com/LeCAR-Lab/human2humanoid | https://openreview.net/forum?id=oL1WEZQal8 | 69 | OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy. Using kinematic pose as a universal control interface, OmniH2O enables various ways for a human to control a f... | [
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corl_2024_oSU7M7MK6B | oSU7M7MK6B | corl | 2,024 | Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions | Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with underactuation, hybrid-dynamics, and frictional uncertainty. Additionally, object occlusions in a scenario of contact uncertainty and where the ... | Juan Del Aguila Ferrandis;Joao Moura;Sethu Vijayakumar | ;University of Edinburgh, University of Edinburgh; | Poster | main | State Estimation;Reinforcement Learning with Tactile Sensing;Non-prehensile Manipulation | https://openreview.net/forum?id=oSU7M7MK6B | 4 | Learning Visuotactile Estimation and Control for Non-prehensile Manipulation under Occlusions
Manipulation without grasping, known as non-prehensile manipulation, is essential for dexterous robots in contact-rich environments, but presents many challenges relating with underactuation, hybrid-dynamics, and frictional un... | [
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corl_2024_ovjxugn9Q2 | ovjxugn9Q2 | corl | 2,024 | SoftManiSim: A Fast Simulation Framework for Multi-Segment Continuum Manipulators Tailored for Robot Learning | This paper introduces SoftManiSim, a novel simulation framework for multi-segment continuum manipulators. Existing continuum robot simulators often rely on simplifying assumptions, such as constant curvature bending or ignoring contact forces, to meet real-time simulation and training demands. To bridge this gap, we pr... | Mohammadreza Kasaei;Hamidreza Kasaei;Mohsen Khadem | ;University of Groningen;Edinburgh University, University of Edinburgh | Poster | main | Simulation Framework;Soft Robotics;Mathematical Modelling;Robot Learning | https://github.com/MohammadKasaei/SoftManiSim | https://openreview.net/forum?id=ovjxugn9Q2 | 1 | SoftManiSim: A Fast Simulation Framework for Multi-Segment Continuum Manipulators Tailored for Robot Learning
This paper introduces SoftManiSim, a novel simulation framework for multi-segment continuum manipulators. Existing continuum robot simulators often rely on simplifying assumptions, such as constant curvature be... | [
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corl_2024_p6Wq6TjjHH | p6Wq6TjjHH | corl | 2,024 | Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph | Learning to plan for multi-step, multi-manipulator tasks is notoriously difficult because of the large search space and the complex constraint satisfaction problems. We present Generative Factor Chaining (GFC), a composable generative model for planning. GFC represents a planning problem as a spatial-temporal factor gr... | Utkarsh Aashu Mishra;Yongxin Chen;Danfei Xu | Toyota Research Institute;Georgia Institute of Technology;NVIDIA | Poster | main | Task and Motion Planning;Manipulation Planning;Bimanual Manipulation;Generative Models | https://openreview.net/forum?id=p6Wq6TjjHH | 3 | Generative Factor Chaining: Coordinated Manipulation with Diffusion-based Factor Graph
Learning to plan for multi-step, multi-manipulator tasks is notoriously difficult because of the large search space and the complex constraint satisfaction problems. We present Generative Factor Chaining (GFC), a composable generativ... | [
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corl_2024_pPhTsonbXq | pPhTsonbXq | corl | 2,024 | GraspSplats: Efficient Manipulation with 3D Feature Splatting | The ability for robots to perform efficient and zero-shot grasping of object parts is crucial for practical applications and is becoming prevalent with recent advances in Vision-Language Models (VLMs). To bridge the 2D-to-3D gap for representations to support such a capability, existing methods rely on neural fields (N... | Mazeyu Ji;Ri-Zhao Qiu;Xueyan Zou;Xiaolong Wang | University of California, San Diego;University of California, San Diego;University of Wisconsin - Madison;University of California, San Diego | Poster | main | Zero-shot manipulation;Gaussian Splatting;Keypoint Tracking | https://github.com/jimazeyu/GraspSplats | https://openreview.net/forum?id=pPhTsonbXq | 16 | GraspSplats: Efficient Manipulation with 3D Feature Splatting
The ability for robots to perform efficient and zero-shot grasping of object parts is crucial for practical applications and is becoming prevalent with recent advances in Vision-Language Models (VLMs). To bridge the 2D-to-3D gap for representations to suppor... | [
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corl_2024_pcPSGZFaCH | pcPSGZFaCH | corl | 2,024 | Modeling the Real World with High-Density Visual Particle Dynamics | We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles. To enable efficiency at this scale, we introduce a novel family of Point Cloud Transformers (PCTs) called Interlac... | William F Whitney;Jake Varley;Deepali Jain;Krzysztof Marcin Choromanski;Sumeet Singh;Vikas Sindhwani | Google DeepMind;Google;Google;Google Brain Robotics & Columbia University;Google Brain Robotics;Google | Poster | main | point clouds;particle dynamics;world models for control;Performers | https://openreview.net/forum?id=pcPSGZFaCH | 1 | Modeling the Real World with High-Density Visual Particle Dynamics
We present High-Density Visual Particle Dynamics (HD-VPD), a learned world model that can emulate the physical dynamics of real scenes by processing massive latent point clouds containing 100K+ particles. To enable efficiency at this scale, we introduc... | [
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corl_2024_qUSa3F79am | qUSa3F79am | corl | 2,024 | Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation | Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks that exploits the semantic understanding of task decomposition provided by vision-langua... | Vivek Myers;Chunyuan Zheng;Oier Mees;Kuan Fang;Sergey Levine | University of California, Berkeley;University of California, Berkeley;Electrical Engineering & Computer Science Department, University of California, Berkeley;;Google | Poster | main | Reinforcement Learning;Vision-Language Models;Manipulation | https://github.com/vivekmyers/palo-robot | https://openreview.net/forum?id=qUSa3F79am | 13 | Policy Adaptation via Language Optimization: Decomposing Tasks for Few-Shot Imitation
Learned language-conditioned robot policies often struggle to effectively adapt to new real-world tasks even when pre-trained across a diverse set of instructions. We propose a novel approach for few-shot adaptation to unseen tasks th... | [
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corl_2024_qoebyrnF36 | qoebyrnF36 | corl | 2,024 | Control with Patterns: A D-learning Method | Learning-based control policies are widely used in various tasks in the field of robotics and control. However, formal (Lyapunov) stability guarantees for learning-based controllers with nonlinear dynamical systems are challenging to obtain.
We propose a novel control approach, namely Control with Patterns (CWP), to a... | Quan Quan;Kai-Yuan Cai;Chenyu Wang | Beihang University;Beihang University;Beihang University | Poster | main | Lyapunov Methods;Reinforcement Learning;Control with Patterns;D-learning;Visual Servoing | https://openreview.net/forum?id=qoebyrnF36 | 0 | Control with Patterns: A D-learning Method
Learning-based control policies are widely used in various tasks in the field of robotics and control. However, formal (Lyapunov) stability guarantees for learning-based controllers with nonlinear dynamical systems are challenging to obtain.
We propose a novel control approac... | [
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corl_2024_r6ZhiVYriY | r6ZhiVYriY | corl | 2,024 | Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction | Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks. In this paper, we examine the topic of LLM planning for a set of *continuously parameterized* skills whose exec... | Aidan Curtis;Nishanth Kumar;Jing Cao;Tomás Lozano-Pérez;Leslie Pack Kaelbling | Massachusetts Institute of Technology;The AI Institute;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | LLMs for planning;task and motion planning;constraint satisfaction | https://github.com/Learning-and-Intelligent-Systems/proc3s | https://openreview.net/forum?id=r6ZhiVYriY | 9 | Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction
Recent developments in pretrained large language models (LLMs) applied to robotics have demonstrated their capacity for sequencing a set of discrete skills to achieve open-ended goals in simple robotic tasks. In this paper, w... | [
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corl_2024_rEteJcq61j | rEteJcq61j | corl | 2,024 | Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors | Object-level mapping builds a 3D map of objects in a scene with detailed shapes and poses from multi-view sensor observations. Conventional methods struggle to build complete shapes and estimate accurate poses due to partial occlusions and sensor noise. They require dense observations to cover all objects, which is c... | Ziwei Liao;Binbin Xu;Steven L. Waslander | University of Toronto;University of Toronto;University of Toronto | Poster | main | Mapping;Objects Reconstruction;Pose Estimation;Diffusion | https://github.com/TRAILab/GeneralObjectMapping | https://openreview.net/forum?id=rEteJcq61j | 3 | Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors
Object-level mapping builds a 3D map of objects in a scene with detailed shapes and poses from multi-view sensor observations. Conventional methods struggle to build complete shapes and estimate accurate poses due to partial occlusions and... | [
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corl_2024_rRpmVq6yHv | rRpmVq6yHv | corl | 2,024 | SELFI: Autonomous Self-Improvement with RL for Vision-Based Navigation around People | Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning method, SELFI, that leverages online robot experience to rapidly fine-tune pre-trained control policies efficiently. SELFI applies online model... | Noriaki Hirose;Dhruv Shah;Kyle Stachowicz;Ajay Sridhar;Sergey Levine | Toyota Central R&D Labs., Inc;UC Berkeley;University of California, Berkeley;University of California, Berkeley;Google | Poster | main | online reinforcement learning;vision-based navigation | https://openreview.net/forum?id=rRpmVq6yHv | 2 | SELFI: Autonomous Self-Improvement with RL for Vision-Based Navigation around People
Autonomous self-improving robots that interact and improve with experience are key to the real-world deployment of robotic systems. In this paper, we propose an online learning method, SELFI, that leverages online robot experience to r... | [
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corl_2024_rThtgkXuvZ | rThtgkXuvZ | corl | 2,024 | NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors | Solving complex manipulation tasks in household and factory settings remains challenging due to long-horizon reasoning, fine-grained interactions, and broad object and scene diversity. Learning skills from demonstrations can be an effective strategy, but such methods often have limited generalizability beyond training ... | Shuo Cheng;Caelan Reed Garrett;Ajay Mandlekar;Danfei Xu | Georgia Institute of Technology;NVIDIA;NVIDIA;NVIDIA | Poster | main | Robot Learning;Robot Planning;Manipulation | https://openreview.net/forum?id=rThtgkXuvZ | 0 | NOD-TAMP: Generalizable Long-Horizon Planning with Neural Object Descriptors
Solving complex manipulation tasks in household and factory settings remains challenging due to long-horizon reasoning, fine-grained interactions, and broad object and scene diversity. Learning skills from demonstrations can be an effective st... | [
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corl_2024_rY5T2aIjPZ | rY5T2aIjPZ | corl | 2,024 | DeliGrasp: Inferring Object Properties with LLMs for Adaptive Grasp Policies | Large language models (LLMs) can provide rich physical descriptions of most worldly objects, allowing robots to achieve more informed and capable grasping. We leverage LLMs' common sense physical reasoning and code-writing abilities to infer an object's physical characteristics-mass $m$, friction coefficient $\mu$, and... | William Xie;Maria Valentini;Jensen Lavering;Nikolaus Correll | ;University of Colorado at Boulder;University of Colorado at Boulder;University of Colorado at Boulder | Poster | main | contact-rich manipulation;adaptive grasping;force control;produce manipulation | https://github.com/deligrasp/deligrasp | https://openreview.net/forum?id=rY5T2aIjPZ | 4 | DeliGrasp: Inferring Object Properties with LLMs for Adaptive Grasp Policies
Large language models (LLMs) can provide rich physical descriptions of most worldly objects, allowing robots to achieve more informed and capable grasping. We leverage LLMs' common sense physical reasoning and code-writing abilities to infer a... | [
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corl_2024_rvKWXxIvj0 | rvKWXxIvj0 | corl | 2,024 | Non-rigid Relative Placement through 3D Dense Diffusion | The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug on a mug rack. Recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation; using explicit object-centric geometric reasoning, these approa... | Eric Cai;Octavian Donca;Ben Eisner;David Held | Carnegie Mellon University;;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Deformable;Non-rigid;Manipulation;Relative Placement | https://openreview.net/forum?id=rvKWXxIvj0 | 0 | Non-rigid Relative Placement through 3D Dense Diffusion
The task of "relative placement" is to predict the placement of one object in relation to another, e.g. placing a mug on a mug rack. Recent methods for relative placement have made tremendous progress towards data-efficient learning for robot manipulation; using e... | [
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corl_2024_s0VNSnPeoA | s0VNSnPeoA | corl | 2,024 | Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction | Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approaches typically treat user preferences as a reward, which requires a manual balance between task success and user satisfaction. To integrate new... | Jakob Thumm;Christopher Agia;Marco Pavone;Matthias Althoff | Stanford University;Stanford University;Stanford University;Technische Universität München | Poster | main | Human-Robot Interaction;Human Preference Learning;Task and Motion Planning;Safe Control | https://github.com/JakobThumm/text2interaction | https://openreview.net/forum?id=s0VNSnPeoA | 3 | Text2Interaction: Establishing Safe and Preferable Human-Robot Interaction
Adjusting robot behavior to human preferences can require intensive human feedback, preventing quick adaptation to new users and changing circumstances. Moreover, current approaches typically treat user preferences as a reward, which requires a ... | [
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corl_2024_s0vHSq5QEv | s0vHSq5QEv | corl | 2,024 | Generalizing End-To-End Autonomous Driving In Real-World Environments Using Zero-Shot LLMs | Traditional autonomous driving methods adopt modular design, decomposing tasks into sub-tasks, including perception, prediction, planning, and control. In contrast, end-to-end autonomous driving directly outputs actions from raw sensor data, avoiding error accumulation. However, training an end-to-end model requires a ... | Zeyu Dong;Yimin Zhu;Yansong Li;Kevin Mahon;Yu Sun | State University of New York at Stony Brook;, State University of New York at Stony Brook;University of Illinois Chicago;Sunrise AI Tech;Sunrise Technology Inc. | Poster | main | End-to-end Autonomous Driving;Large Vision-Language Model;Generalization | https://openreview.net/forum?id=s0vHSq5QEv | 5 | Generalizing End-To-End Autonomous Driving In Real-World Environments Using Zero-Shot LLMs
Traditional autonomous driving methods adopt modular design, decomposing tasks into sub-tasks, including perception, prediction, planning, and control. In contrast, end-to-end autonomous driving directly outputs actions from raw ... | [
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corl_2024_s31IWg2kN5 | s31IWg2kN5 | corl | 2,024 | Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters | Applying reinforcement learning (RL) to learn effective policies on physical robots without supervision remains challenging when it comes to tasks where safe exploration is critical. Constrained model-based RL (CMBRL) presents a promising approach to this problem. These methods are designed to learn constraint-adhering... | Ahmed Agha;Baris Kayalibay;Atanas Mirchev;Patrick van der Smagt;Justin Bayer | Volkswagen Group;Data Lab, Volkswagen Group;Machine Learning Research Lab, Volkswagen Group;Machine Learning Research Lab; Volkswagen Group;VW Group | Poster | main | Model-based RL;Safe RL;Safety Filter;Exploration | https://openreview.net/forum?id=s31IWg2kN5 | 3 | Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters
Applying reinforcement learning (RL) to learn effective policies on physical robots without supervision remains challenging when it comes to tasks where safe exploration is critical. Constrained model-based RL (CMBRL) presents a promising appr... | [
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corl_2024_t0LkF9JnVb | t0LkF9JnVb | corl | 2,024 | PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations | In this work, we introduce PianoMime, a framework for training a piano-playing agent using internet demonstrations.
The internet is a promising source of large-scale demonstrations for training our robot agents.
In particular, for the case of piano-playing, Youtube is full of videos of professional pianists playing a ... | Cheng Qian;Julen Urain;Kevin Zakka;Jan Peters | Technische Universität München;University of California, Berkeley;TU Darmstadt;Technische Universität Darmstadt | Poster | main | Reinforcement Learning;Imitation Learning;Robotics;Dexterous Manipulation | https://github.com/sNiper-Qian/pianomime | https://openreview.net/forum?id=t0LkF9JnVb | 7 | PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations
In this work, we introduce PianoMime, a framework for training a piano-playing agent using internet demonstrations.
The internet is a promising source of large-scale demonstrations for training our robot agents.
In particular, for th... | [
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corl_2024_tqsQGrmVEu | tqsQGrmVEu | corl | 2,024 | View-Invariant Policy Learning via Zero-Shot Novel View Synthesis | Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, environments, and observational modalities remain elusive.
In this work, we investigate how knowledge from large-scale visual data of the world... | Stephen Tian;Blake Wulfe;Kyle Sargent;Katherine Liu;Sergey Zakharov;Vitor Campagnolo Guizilini;Jiajun Wu | Stanford University;;Computer Science Department, Stanford University;Toyota Research Institute;Toyota Research Institute;Toyota Research Institute;Stanford University | Poster | main | generalization;visual imitation learning;view synthesis | https://github.com/s-tian/VISTA | https://openreview.net/forum?id=tqsQGrmVEu | 10 | View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, environments, and observational modalities remain elusive.
In this work, we i... | [
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corl_2024_ty1cqzTtUv | ty1cqzTtUv | corl | 2,024 | RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches | Natural language and images are commonly used as goal representations in goal-conditioned imitation learning. However, language can be ambiguous and images can be over-specified. In this work, we study hand-drawn sketches as a modality for goal specification. Sketches can be easy to provide on the fly like language, bu... | Priya Sundaresan;Quan Vuong;Jiayuan Gu;Peng Xu;Ted Xiao;Sean Kirmani;Tianhe Yu;Michael Stark;Ajinkya Jain;Karol Hausman;Dorsa Sadigh;Jeannette Bohg;Stefan Schaal | Stanford University;physical intelligence;University of California, San Diego;Google;;Google DeepMind;Google Brain;;Intrinsic Innovation LLC;;Stanford University;Stanford University; | Poster | main | Visual Imitation Learning;Goal-Conditioned Manipulation | https://openreview.net/forum?id=ty1cqzTtUv | 11 | RT-Sketch: Goal-Conditioned Imitation Learning from Hand-Drawn Sketches
Natural language and images are commonly used as goal representations in goal-conditioned imitation learning. However, language can be ambiguous and images can be over-specified. In this work, we study hand-drawn sketches as a modality for goal spe... | [
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corl_2024_uEbJXWobif | uEbJXWobif | corl | 2,024 | EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data | Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks.
Instead, RL agents that can act over useful, temporally extended skills rather than low-leve... | Jesse Zhang;Minho Heo;Zuxin Liu;Erdem Biyik;Joseph J Lim;Yao Liu;Rasool Fakoor | NVIDIA;Korea Advanced Institute of Science & Technology;Salesforce AI Research;University of Southern California;Korea Advanced Institute of Science & Technology;Amazon;Amazon Web Services | Poster | main | reinforcement learning;skill-based reinformement learning;skill learning;transfer learning;foundation models for robotics;robot learning | https://openreview.net/forum?id=uEbJXWobif | 2 | EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data
Most reinforcement learning (RL) methods focus on learning optimal policies over low-level action spaces. While these methods can perform well in their training environments, they lack the flexibility to transfer to new tasks. ... | [
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corl_2024_uHdVI3QMr6 | uHdVI3QMr6 | corl | 2,024 | A Dual Approach to Imitation Learning from Observations with Offline Datasets | Demonstrations are an effective alternative to task specification for learning agents in settings where designing a reward function is difficult. However, demonstrating expert behavior in the action space of the agent becomes unwieldy when robots have complex, unintuitive morphologies. We consider the practical setting... | Harshit Sikchi;Caleb Chuck;Amy Zhang;Scott Niekum | University of Texas, Austin;University of Texas, Austin;University of Massachusetts at Amherst;Meta Facebook | Poster | main | Learning from Observations;Imitation Learning | https://github.com/hari-sikchi/DILO | https://openreview.net/forum?id=uHdVI3QMr6 | 3 | A Dual Approach to Imitation Learning from Observations with Offline Datasets
Demonstrations are an effective alternative to task specification for learning agents in settings where designing a reward function is difficult. However, demonstrating expert behavior in the action space of the agent becomes unwieldy when ro... | [
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corl_2024_uJBMZ6S02T | uJBMZ6S02T | corl | 2,024 | Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection | For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, this way explicitly or implicitly forces the simulated data to adapt to the noisy real data... | Jia-Feng Cai;Zibo Chen;Xiao-Ming Wu;Jian-Jian Jiang;Yi-Lin Wei;Wei-Shi Zheng | SUN YAT-SEN UNIVERSITY;SUN YAT-SEN UNIVERSITY;Macquarie University;SUN YAT-SEN UNIVERSITY;SUN YAT-SEN UNIVERSITY;SUN YAT-SEN UNIVERSITY | Poster | main | Grasp pose detection;simulated datasets;sim-to-real | https://openreview.net/forum?id=uJBMZ6S02T | 4 | Real-to-Sim Grasp: Rethinking the Gap between Simulation and Real World in Grasp Detection
For 6-DoF grasp detection, simulated data is expandable to train more powerful model, but it faces the challenge of the large gap between simulation and real world. Previous works bridge this gap with a sim-to-real way. However, ... | [
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corl_2024_uMZ2jnZUDX | uMZ2jnZUDX | corl | 2,024 | Learning H-Infinity Locomotion Control | Stable locomotion in precipitous environments is an essential task for quadruped robots, requiring the ability to resist various external disturbances. Recent neural policies enhance robustness against disturbances by learning to resist external forces sampled from a fixed distribution in the simulated environment. How... | Junfeng Long;Wenye Yu;Quanyi Li;ZiRui Wang;Dahua Lin;Jiangmiao Pang | Shanghai AI Laboratory;Shanghai Jiaotong University;University of Edinburgh;The Chinese University of Hong Kong;Shanghai AI Laboratory ;Shanghai Artificial Intelligence Laboratory | Poster | main | Robot Learning;Quadrupedal Robot;Robust Locomotion | https://openreview.net/forum?id=uMZ2jnZUDX | 9 | Learning H-Infinity Locomotion Control
Stable locomotion in precipitous environments is an essential task for quadruped robots, requiring the ability to resist various external disturbances. Recent neural policies enhance robustness against disturbances by learning to resist external forces sampled from a fixed distrib... | [
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corl_2024_ubq7Co6Cbv | ubq7Co6Cbv | corl | 2,024 | Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks | Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories.
However, transferring behavior learned from simulation data into the real world proves to be difficult, usually mitigated with compute-heavy domain randomization method... | Alex Quach;Makram Chahine;Alexander Amini;Ramin Hasani;Daniela Rus | Liquid AI;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology | Poster | main | End-to-end learning;Gaussian Splatting;Sim-to-real transfer | https://github.com/alexquach/multienv_sim | https://openreview.net/forum?id=ubq7Co6Cbv | 7 | Gaussian Splatting to Real World Flight Navigation Transfer with Liquid Networks
Simulators are powerful tools for autonomous robot learning as they offer scalable data generation, flexible design, and optimization of trajectories.
However, transferring behavior learned from simulation data into the real world proves ... | [
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corl_2024_ueBmGhLOXP | ueBmGhLOXP | corl | 2,024 | EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning | Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning. We propose EquiBot, a robust, data-efficient, and generalizable approach for robot manipulation task learning. Our approac... | Jingyun Yang;Ziang Cao;Congyue Deng;Rika Antonova;Shuran Song;Jeannette Bohg | Stanford University;;Stanford University;;Stanford University;Stanford University | Poster | main | Imitation Learning;Equivariance;Data Efficiency | https://github.com/yjy0625/equibot | https://openreview.net/forum?id=ueBmGhLOXP | 38 | EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning
Building effective imitation learning methods that enable robots to learn from limited data and still generalize across diverse real-world environments is a long-standing problem in robot learning. We propose EquiBot, a robust, d... | [
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corl_2024_vBj5oC60Lk | vBj5oC60Lk | corl | 2,024 | Lifelong Autonomous Improvement of Navigation Foundation Models in the Wild | Recent works have proposed a number of general-purpose robotic foundation models that can control a variety of robotic platforms to perform a range of different tasks, including in the domains of navigation and manipulation. However, such models are typically trained via imitation learning, which precludes the ability ... | Kyle Stachowicz;Lydia Ignatova;Sergey Levine | University of California, Berkeley;University of California, Berkeley;Google | Poster | main | Navigation;Reinforcement Learning;Lifelong Learning | https://github.com/kylestach/lifelong-nav-rl | https://openreview.net/forum?id=vBj5oC60Lk | 2 | Lifelong Autonomous Improvement of Navigation Foundation Models in the Wild
Recent works have proposed a number of general-purpose robotic foundation models that can control a variety of robotic platforms to perform a range of different tasks, including in the domains of navigation and manipulation. However, such model... | [
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corl_2024_vhGkyWgctu | vhGkyWgctu | corl | 2,024 | Learning Decentralized Multi-Biped Control for Payload Transport | Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitable for legs rather than wheels. For this purpose, we consider mul... | Bikram Pandit;Ashutosh Gupta;Mohitvishnu S. Gadde;Addison Johnson;Aayam Kumar Shrestha;Helei Duan;Jeremy Dao;Alan Fern | Oregon State University;Oregon State University;Oregon State University;;Oregon State University;;Oregon State University;Oregon State University | Poster | main | Multi-robot Transport;Bipedal locomotion;Reinforcement Learning | https://github.com/osudrl/roadrunner/tree/paper/decmbc | https://openreview.net/forum?id=vhGkyWgctu | 4 | Learning Decentralized Multi-Biped Control for Payload Transport
Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitab... | [
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corl_2024_vobaOY0qDl | vobaOY0qDl | corl | 2,024 | Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation | Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specific systems and often... | Jan Bruedigam;Ali Adeeb Abbas;Maks Sorokin;Kuan Fang;Brandon Hung;Maya Guru;Stefan Georg Sosnowski;Jiuguang Wang;Sandra Hirche;Simon Le Cleac'h | Technische Universität München;;;;;;;;Technical University Munich; | Poster | main | Dexterous Manipulation Planning;Learning with Demonstrations | https://openreview.net/forum?id=vobaOY0qDl | 2 | Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typical... | [
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corl_2024_vtEn8NJWlz | vtEn8NJWlz | corl | 2,024 | Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching | Learning from expert demonstrations is a popular approach to train
robotic manipulation policies from limited data. However, imitation learning
algorithms require a number of design choices ranging from the input modality,
training objective, and 6-DoF end-effector pose representation. Diffusion-based
methods have gain... | Eugenio Chisari;Nick Heppert;Max Argus;Tim Welschehold;Thomas Brox;Abhinav Valada | Universität Freiburg;University of Freiburg, Albert-Ludwigs-Universität Freiburg;Universität Freiburg;University of Freiburg;University of Freiburg;University of Freiburg, Albert-Ludwigs-Universität Freiburg | Poster | main | Imitation Learning;Manipulation;Conditional Flow Matching | https://openreview.net/forum?id=vtEn8NJWlz | 14 | Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
Learning from expert demonstrations is a popular approach to train
robotic manipulation policies from limited data. However, imitation learning
algorithms require a number of design choices ranging from the input modality,
training ... | [
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corl_2024_wD2kUVLT1g | wD2kUVLT1g | corl | 2,024 | Equivariant Diffusion Policy | Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an explicit policy. In this wor... | Dian Wang;Stephen Hart;David Surovik;Tarik Kelestemur;Haojie Huang;Haibo Zhao;Mark Yeatman;Jiuguang Wang;Robin Walters;Robert Platt | Northeastern University;The Robotics & AI Institute;The AI Institute ;Boston Dynamics AI Institute;Northeastern University;Northeastern University;;;Northeastern University ;Northeastern University | Poster | main | Equivariance;Diffusion Model;Robotic Manipulation | https://openreview.net/forum?id=wD2kUVLT1g | 26 | Equivariant Diffusion Policy
Recent work has shown diffusion models are an effective approach to learning the multimodal distributions arising from demonstration data in behavior cloning. However, a drawback of this approach is the need to learn a denoising function, which is significantly more complex than learning an... | [
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corl_2024_wH7Wv0nAm8 | wH7Wv0nAm8 | corl | 2,024 | Bi-Level Motion Imitation for Humanoid Robots | Imitation learning from human motion capture (MoCap) data provides a promising way to train humanoid robots. However, due to differences in morphology, such as varying degrees of joint freedom and force limits, exact replication of human behaviors may not be feasible for humanoid robots. Consequently, incorporating phy... | Wenshuai Zhao;Yi Zhao;Joni Pajarinen;Michael Muehlebach | Aalto University;Max Planck Institute for Intelligent Systems;Aalto University;Max-Planck Institute | Poster | main | Humanoid Robots;Imitation Learning;Latent Dynamics Model | https://openreview.net/forum?id=wH7Wv0nAm8 | 2 | Bi-Level Motion Imitation for Humanoid Robots
Imitation learning from human motion capture (MoCap) data provides a promising way to train humanoid robots. However, due to differences in morphology, such as varying degrees of joint freedom and force limits, exact replication of human behaviors may not be feasible for hu... | [
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corl_2024_wSWMsjuMTI | wSWMsjuMTI | corl | 2,024 | ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data | Audio signals provide rich information for the robot interaction and object properties through contact. These information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ambiguous or incomplete. However, the usage of audio data in robot manip... | Zeyi Liu;Cheng Chi;Eric Cousineau;Naveen Kuppuswamy;Benjamin Burchfiel;Shuran Song | Stanford University;Stanford University;Toyota Research Institute;Toyota Research Institute;Dexterous Manipulation Group, Toyota Research Institute;Stanford University | Poster | main | Robot Manipulation;Imitation Learning;Audio | https://github.com/real-stanford/maniwav | https://openreview.net/forum?id=wSWMsjuMTI | 24 | ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data
Audio signals provide rich information for the robot interaction and object properties through contact. These information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ... | [
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corl_2024_wTKJge0PTq | wTKJge0PTq | corl | 2,024 | HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers | Large Vision-Language-Action (VLA) models, leveraging powerful pre-trained Vision-Language Models (VLMs) backends, have shown promise in robotic control due to their impressive generalization ability. However, the success comes at a cost. Their reliance on VLM backends with billions of parameters leads to high computat... | Jianke Zhang;Yanjiang Guo;Xiaoyu Chen;Yen-Jen Wang;Yucheng Hu;Chengming Shi;Jianyu Chen | Beijing Institute of Technology;Tsinghua University;Tsinghua University;Tsinghua University;Tsinghua University;;Tsinghua University | Poster | main | Imitation Learning;Robots;Vision Language Models | https://openreview.net/forum?id=wTKJge0PTq | 8 | HiRT: Enhancing Robotic Control with Hierarchical Robot Transformers
Large Vision-Language-Action (VLA) models, leveraging powerful pre-trained Vision-Language Models (VLMs) backends, have shown promise in robotic control due to their impressive generalization ability. However, the success comes at a cost. Their relian... | [
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corl_2024_wcbrhPnOei | wcbrhPnOei | corl | 2,024 | RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards | This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our proposed framework u... | Fatemeh Zargarbashi;Jin Cheng;Dongho Kang;Robert Sumner;Stelian Coros | Disney Research|Studios;ETHZ - ETH Zurich;ETHZ - ETH Zurich;Disney Research, Disney Research;ETHZ - ETH Zurich | Poster | main | Legged robots;Multi-Critic Reinforcement Learning;Motion Imitation | https://openreview.net/forum?id=wcbrhPnOei | 7 | RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards
This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable ... | [
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corl_2024_xYJn2e1uu8 | xYJn2e1uu8 | corl | 2,024 | Sparsh: Self-supervised touch representations for vision-based tactile sensing | In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advances in robot manipulation as they markedly complement vision, yet solutions today often rely on task and sensor specific handcrafted perceptio... | Carolina Higuera;Akash Sharma;Chaithanya Krishna Bodduluri;Taosha Fan;Patrick Lancaster;Mrinal Kalakrishnan;Michael Kaess;Byron Boots;Mike Lambeta;Tingfan Wu;Mustafa Mukadam | University of Washington;Carnegie Mellon University;Meta Facebook;;Meta;Meta;Carnegie Mellon University;;Meta;;Meta AI | Poster | main | Tactile sensing;Pre-trained representations;Self-supervised learning | https://github.com/facebookresearch/sparsh | https://openreview.net/forum?id=xYJn2e1uu8 | 10 | Sparsh: Self-supervised touch representations for vision-based tactile sensing
In this work, we introduce general purpose touch representations for the increasingly accessible class of vision-based tactile sensors. Such sensors have led to many recent advances in robot manipulation as they markedly complement vision, y... | [
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corl_2024_xYleTh2QhS | xYleTh2QhS | corl | 2,024 | Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation | Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains.
The effectiveness of these methods hinges on two essential elements:
(1) the use of massively parallel physics simulations to expedite policy ... | Youwei Yu;Junhong Xu;Lantao Liu | Indiana University;Indiana University, Bloomington;Indiana University, Bloomington | Poster | main | Curriculum Reinforcement Learning;Diffusion Model;Field Robotics | https://openreview.net/forum?id=xYleTh2QhS | 3 | Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain Navigation
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured terrains.
The effectiveness of these methods hinges on two essential elemen... | [
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corl_2024_xcBH8Jhmbi | xcBH8Jhmbi | corl | 2,024 | Discovering Robotic Interaction Modes with Discrete Representation Learning | Abstract: Human actions manipulating articulated objects, such as opening and closing a drawer, can be categorized into multiple modalities we define as interaction modes. Traditional robot learning approaches lack discrete representations of these modes, which are crucial for empirical sampling and grounding. In this ... | Liquan Wang;Ankit Goyal;Haoping Xu;Animesh Garg | Department of Computer Science;NVIDIA;Toronto University;NVIDIA | Poster | main | Discovering Robotic Interaction Modes with Discrete Representation Learning | https://github.com/pairlab/ActAIM.git | https://openreview.net/forum?id=xcBH8Jhmbi | 1 | Discovering Robotic Interaction Modes with Discrete Representation Learning
Abstract: Human actions manipulating articulated objects, such as opening and closing a drawer, can be categorized into multiple modalities we define as interaction modes. Traditional robot learning approaches lack discrete representations of t... | [
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corl_2024_xeFKtSXPMd | xeFKtSXPMd | corl | 2,024 | OCCAM: Online Continuous Controller Adaptation with Meta-Learned Models | Control tuning and adaptation present a significant challenge to the usage of robots in diverse environments. It is often nontrivial to find a single set of control parameters by hand that work well across the broad array of environments and conditions that a robot might encounter. Automated adaptation approaches must ... | Hersh Sanghvi;Spencer Folk;Camillo Jose Taylor | University of Pennsylvania;University of Pennsylvania;University of Pennsylvania | Poster | main | Controller Adaptation;Robot Model Learning;Meta-Learning | https://openreview.net/forum?id=xeFKtSXPMd | 2 | OCCAM: Online Continuous Controller Adaptation with Meta-Learned Models
Control tuning and adaptation present a significant challenge to the usage of robots in diverse environments. It is often nontrivial to find a single set of control parameters by hand that work well across the broad array of environments and condit... | [
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corl_2024_y8XkuQIrvI | y8XkuQIrvI | corl | 2,024 | MILES: Making Imitation Learning Easy with Self-Supervision | Data collection in imitation learning often requires significant, laborious human supervision, such as numerous demonstrations, and/or frequent environment resets for methods that incorporate reinforcement learning. In this work, we propose an alternative approach, MILES: a fully autonomous, self-supervised data collec... | Georgios Papagiannis;Edward Johns | Imperial College London;Imperial College London | Poster | main | Imitation Learning;Robotic Manipulation;Self-Supervised Data Collection | https://openreview.net/forum?id=y8XkuQIrvI | 3 | MILES: Making Imitation Learning Easy with Self-Supervision
Data collection in imitation learning often requires significant, laborious human supervision, such as numerous demonstrations, and/or frequent environment resets for methods that incorporate reinforcement learning. In this work, we propose an alternative appr... | [
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corl_2024_yNQu9zqx6X | yNQu9zqx6X | corl | 2,024 | Robust Manipulation Primitive Learning via Domain Contraction | Contact-rich manipulation plays an important role in everyday life, but uncertain parameters pose significant challenges to model-based planning and control. To address this issue, domain adaptation and domain randomization have been proposed to learn robust policies. However, they either lose the generalization abilit... | Teng Xue;Amirreza Razmjoo;Suhan Shetty;Sylvain Calinon | Idiap Research Institute;;EPFL - EPF Lausanne;EPFL - EPF Lausanne | Poster | main | Robust policy learning;Contact-rich manipulation;Sim-to-real | https://openreview.net/forum?id=yNQu9zqx6X | 3 | Robust Manipulation Primitive Learning via Domain Contraction
Contact-rich manipulation plays an important role in everyday life, but uncertain parameters pose significant challenges to model-based planning and control. To address this issue, domain adaptation and domain randomization have been proposed to learn robust... | [
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corl_2024_ySI0tBYxpz | ySI0tBYxpz | corl | 2,024 | Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion | The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or learn a distribution across gaits using complex black-box models. Alternatively, we present Gaitor, which learns a disentangled and 2D represen... | Alexander Luis Mitchell;Wolfgang Merkt;Aristotelis Papatheodorou;Ioannis Havoutis;Ingmar Posner | University of Oxford;University of Oxford, University of Oxford;University of Oxford;;University of Oxford | Poster | main | Representation Learning;Learning for Control;Quadruped Control | https://openreview.net/forum?id=ySI0tBYxpz | 3 | Gaitor: Learning a Unified Representation Across Gaits for Real-World Quadruped Locomotion
The current state-of-the-art in quadruped locomotion is able to produce a variety of complex motions. These methods either rely on switching between a discrete set of skills or learn a distribution across gaits using complex blac... | [
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corl_2024_yYujuPxjDK | yYujuPxjDK | corl | 2,024 | Language-guided Manipulator Motion Planning with Bounded Task Space | Language-based robot control is a powerful and versatile method to control a robot manipulator where large language models (LLMs) are used to reason about the environment. However, the generated robot motions by these controllers often lack safety and performance, resulting in jerky movements. In this work, a novel mod... | Thies Oelerich;Christian Hartl-Nesic;Andreas Kugi | Technische Universität Wien;;Technische Universität Wien | Poster | main | Vision Language Models;Manipulation Planning;Path-following MPC | https://openreview.net/forum?id=yYujuPxjDK | 3 | Language-guided Manipulator Motion Planning with Bounded Task Space
Language-based robot control is a powerful and versatile method to control a robot manipulator where large language models (LLMs) are used to reason about the environment. However, the generated robot motions by these controllers often lack safety and ... | [
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corl_2024_ylZHvlwUcI | ylZHvlwUcI | corl | 2,024 | Theia: Distilling Diverse Vision Foundation Models for Robot Learning | Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification or segmentation. Inspired by this, we introduce Theia, a vision foundation model for robot learning that distills multiple off-the-shelf vis... | Jinghuan Shang;Karl Schmeckpeper;Brandon B. May;Maria Vittoria Minniti;Tarik Kelestemur;David Watkins;Laura Herlant | Department of Computer Science, State University of New York, Stony Brook;The Robotics and AI Institute;;The AI Institute;Boston Dynamics AI Institute;;The Robotics and AI Institute | Poster | main | visual representation;robot learning;distillation;foundation model | https://github.com/bdaiinstitute/theia | https://openreview.net/forum?id=ylZHvlwUcI | 18 | Theia: Distilling Diverse Vision Foundation Models for Robot Learning
Vision-based robot policy learning, which maps visual inputs to actions, necessitates a holistic understanding of diverse visual tasks beyond single-task needs like classification or segmentation. Inspired by this, we introduce Theia, a vision founda... | [
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corl_2024_ypaYtV1CoG | ypaYtV1CoG | corl | 2,024 | Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration | We introduce Vocal Sandbox, a framework for enabling seamless human-robot collaboration in situated environments. Systems in our framework are characterized by their ability to *adapt and continually learn* at multiple levels of abstraction from diverse teaching modalities such as spoken dialogue, object keypoints, and... | Jennifer Grannen;Siddharth Karamcheti;Suvir Mirchandani;Percy Liang;Dorsa Sadigh | Computer Science Department, Stanford University;Stanford University;Stanford University;Stanford University;Stanford University | Poster | main | Continual Learning;Multimodal Teaching;Human-Robot Interaction | https://openreview.net/forum?id=ypaYtV1CoG | 0 | Vocal Sandbox: Continual Learning and Adaptation for Situated Human-Robot Collaboration
We introduce Vocal Sandbox, a framework for enabling seamless human-robot collaboration in situated environments. Systems in our framework are characterized by their ability to *adapt and continually learn* at multiple levels of abs... | [
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corl_2024_yqLFb0RnDW | yqLFb0RnDW | corl | 2,024 | Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress | Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies at test time and provide early warnings of failure are necessary to facilitate scalable deployment. We propose Sentinel, a runtime monitoring... | Christopher Agia;Rohan Sinha;Jingyun Yang;Ziang Cao;Rika Antonova;Marco Pavone;Jeannette Bohg | Stanford University;Stanford University;Stanford University;;;Stanford University;Stanford University | Poster | main | Failure Detection;Generative Policies;Vision Language Models | https://openreview.net/forum?id=yqLFb0RnDW | 9 | Unpacking Failure Modes of Generative Policies: Runtime Monitoring of Consistency and Progress
Robot behavior policies trained via imitation learning are prone to failure under conditions that deviate from their training data. Thus, algorithms that monitor learned policies at test time and provide early warnings of fai... | [
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corl_2024_zIWu9Kmlqk | zIWu9Kmlqk | corl | 2,024 | LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Video | We present our method, LeLaN, which uses action-free egocentric data to learn robust language-conditioned object navigation. By leveraging the knowledge of large vision and language models and grounding this knowledge using pre-trained segmentation and depth estimation models, we can label in-the-wild data from a varie... | Noriaki Hirose;Catherine Glossop;Ajay Sridhar;Oier Mees;Sergey Levine | Toyota Central R&D Labs., Inc;University of California, Berkeley;University of California, Berkeley;Electrical Engineering & Computer Science Department, University of California, Berkeley;Google | Poster | main | Language-conditioned navigation policy;data augmentation | https://github.com/NHirose/learning-language-navigation | https://openreview.net/forum?id=zIWu9Kmlqk | 7 | LeLaN: Learning A Language-Conditioned Navigation Policy from In-the-Wild Video
We present our method, LeLaN, which uses action-free egocentric data to learn robust language-conditioned object navigation. By leveraging the knowledge of large vision and language models and grounding this knowledge using pre-trained segm... | [
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corl_2024_zeYaLS2tw5 | zeYaLS2tw5 | corl | 2,024 | Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning | The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all tasks, facing challenges such as high computational costs and catastrophic forgetting when learning new tasks. To address these issues, we int... | Yixiao Wang;Yifei Zhang;Mingxiao Huo;Thomas Tian;Xiang Zhang;Yichen Xie;Chenfeng Xu;Pengliang Ji;Wei Zhan;Mingyu Ding;Masayoshi Tomizuka | University of California, Berkeley;University of Chinese Academy of Sciences;Carnegie Mellon University;University of California, Berkeley;University of California, Berkeley;Waymo;University of California, Berkeley;;;University of California, Berkeley; | Poster | main | Robot Policy;Multitask;Continual learning;Mixture of Experts | https://github.com/AnthonyHuo/SDP | https://openreview.net/forum?id=zeYaLS2tw5 | 17 | Sparse Diffusion Policy: A Sparse, Reusable, and Flexible Policy for Robot Learning
The increasing complexity of tasks in robotics demands efficient strategies for multitask and continual learning. Traditional models typically rely on a universal policy for all tasks, facing challenges such as high computational costs ... | [
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corl_2024_zr2GPi3DSb | zr2GPi3DSb | corl | 2,024 | Gentle Manipulation of Tree Branches: A Contact-Aware Policy Learning Approach | Learning to interact with deformable tree branches with minimal damage is challenging due to their intricate geometry and inscrutable dynamics. Furthermore, traditional vision-based modelling systems suffer from implicit occlusions in dense foliage, severely changing lighting conditions, and limited field of view, in a... | Jay Jacob;Shizhe Cai;Paulo Vinicius Koerich Borges;Tirthankar Bandyopadhyay;Fabio Ramos | ;University of Sydney;CSIRO;, CSIRO;NVIDIA | Poster | main | Reinforcement Learning;Sim-to-Real;Deformable Manipulation | https://openreview.net/forum?id=zr2GPi3DSb | 3 | Gentle Manipulation of Tree Branches: A Contact-Aware Policy Learning Approach
Learning to interact with deformable tree branches with minimal damage is challenging due to their intricate geometry and inscrutable dynamics. Furthermore, traditional vision-based modelling systems suffer from implicit occlusions in dense ... | [
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corl_2025_06YyNxzwae | 06YyNxzwae | corl | 2,025 | Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference | Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about and learn from failures is necessary. We investigate the problem of efficiently generating a dataset targeted to observed failures. After fin... | Yixuan Huang;Novella Alvina;Mohanraj Devendran Shanthi;Tucker Hermans | Princeton University+University of Utah;University of Utah;, University of Utah;NVIDIA+University of Utah | Poster | main | Learning from failures;Variational inference;Skill effect models | https://openreview.net/forum?id=06YyNxzwae | -1 | Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference
Skill effect models for long-horizon manipulation tasks are prone to failures in conditions not covered by training data distributions. Therefore, enabling robots to reason about and learn from failures is necessary. We investigate ... | [
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corl_2025_0ViTEgiFiQ | 0ViTEgiFiQ | corl | 2,025 | Disentangled Multi-Context Meta-Learning: Unlocking Robust and Generalized Task Learning | In meta-learning and its downstream tasks, many methods use implicit adaptation to represent task-specific variations. However, implicit approaches hinder interpretability and make it difficult to understand which task factors drive performance. In this work, we introduce a disentangled multi-context meta-learning fram... | Seonsoo Kim;Jun-Gill Kang;Taehong Kim;Seongil Hong | Agency for Defense Development;Agency For Defense Development;Agency for defense development;Agency for Defense Development | Poster | main | Meta-Learning;Multi Task Learning;Quadruped Robot Locomotion | https://openreview.net/forum?id=0ViTEgiFiQ | -1 | Disentangled Multi-Context Meta-Learning: Unlocking Robust and Generalized Task Learning
In meta-learning and its downstream tasks, many methods use implicit adaptation to represent task-specific variations. However, implicit approaches hinder interpretability and make it difficult to understand which task factors driv... | [
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corl_2025_19LSN4QnV4 | 19LSN4QnV4 | corl | 2,025 | FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection | In order to navigate complex traffic environments, self-driving vehicles
must recognize many semantic classes pertaining to vulnerable road users or
traffic control devices. However, many safety-critical objects (e.g.,
construction worker) appear infrequently in nominal traffic conditions, leading to a
severe shortage... | Anqi Joyce Yang;James Tu;Nikita Dvornik;Enxu Li;Raquel Urtasun | University of Toronto+Waabi Innovation Inc;Department of Computer Science, University of Toronto;Palona AI;Department of Computer Science, University of Toronto+Waabi;Waabi+Department of Computer Science, University of Toronto | Poster | main | Long-Tailed 3D Object Detection;Vision Foundation Model;Multimodal Fusion;Autonomous Vehicles | https://openreview.net/forum?id=19LSN4QnV4 | -1 | FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
In order to navigate complex traffic environments, self-driving vehicles
must recognize many semantic classes pertaining to vulnerable road users or
traffic control devices. However, many safety-critical objects (e.g.,
construction worker) app... | [
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corl_2025_1D6XYy6ofW | 1D6XYy6ofW | corl | 2,025 | LaVA-Man: Learning Visual Action Representations for Robot Manipulation | Visual-textual understanding is essential for language-guided robot manipulation. Recent works leverage pre-trained vision-language models to measure the similarity between encoded visual observations and textual instructions, and then train a model to map this similarity to robot actions. However, this two-step approa... | Chaoran Zhu;Hengyi Wang;Yik Lung Pang;Changjae Oh | Queen Mary University of London;University College London;; | Poster | main | Robot manipulation;self-supervised representation learning | https://openreview.net/forum?id=1D6XYy6ofW | -1 | LaVA-Man: Learning Visual Action Representations for Robot Manipulation
Visual-textual understanding is essential for language-guided robot manipulation. Recent works leverage pre-trained vision-language models to measure the similarity between encoded visual observations and textual instructions, and then train a mode... | [
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corl_2025_1HW2UhshIT | 1HW2UhshIT | corl | 2,025 | Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions | Robot-assisted dressing has the potential to significantly improve the lives of individuals with mobility impairments. To ensure an effective and comfortable dressing experience, the robot must be able to handle challenging deformable garments, apply appropriate forces, and adapt to limb movements throughout the dressi... | Alexis Yihong Hao;Yufei Wang;Navin Sriram Ravie;Bharath Hegde;David Held;Zackory Erickson | Carnegie Mellon University;School of Computer Science, Carnegie Mellon University;Indian Institute of Technology Madras;Carnegie Mellon University;Carnegie Mellon University;Carnegie Mellon University | Poster | main | Robot-Assisted Dressing;Multi-Modal Learning;Physical Human Robot Interaction;Deformable Object Manipulation | https://openreview.net/forum?id=1HW2UhshIT | -1 | Force-Modulated Visual Policy for Robot-Assisted Dressing with Arm Motions
Robot-assisted dressing has the potential to significantly improve the lives of individuals with mobility impairments. To ensure an effective and comfortable dressing experience, the robot must be able to handle challenging deformable garments, ... | [
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corl_2025_1K3kjo91Q1 | 1K3kjo91Q1 | corl | 2,025 | Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames | Imitation learning has unlocked the potential for robots to exhibit highly dexterous behaviours. However, it still struggles with long-horizon, multi-object tasks due to poor sample efficiency and limited generalisation. Existing methods require a substantial number of demonstrations to cover possible task variations, ... | Krishan Rana;Jad Abou-Chakra;Sourav Garg;Robert Lee;Ian Reid;Niko Suenderhauf | Queensland University of Technology;The AI Institute+Queensland University of Technology;University of Adelaide;Woven By Toyota, Inc.;Mohamed bin Zayed University of Artificial Intelligence+University of Adelaide;Queensland University of Technology | Poster | main | behaviour cloning;spatial generalisation;intra-category generalisation;long-horizon tasks;affordances | https://openreview.net/forum?id=1K3kjo91Q1 | -1 | Learning from 10 Demos: Generalisable and Sample-Efficient Policy Learning with Oriented Affordance Frames
Imitation learning has unlocked the potential for robots to exhibit highly dexterous behaviours. However, it still struggles with long-horizon, multi-object tasks due to poor sample efficiency and limited generali... | [
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corl_2025_1NBdplgILy | 1NBdplgILy | corl | 2,025 | Vision in Action: Learning Active Perception from Human Demonstrations | We present Vision in Action (ViA), an active perception system for bimanual robot manipulation. ViA learns task-relevant active perceptual strategies (e.g., searching, tracking, and focusing) directly from human demonstrations. On the hardware side, ViA employs a simple yet effective 6-DoF robotic neck to enable flexib... | Haoyu Xiong;Xiaomeng Xu;Jimmy Wu;Yifan Hou;Jeannette Bohg;Shuran Song | Massachusetts Institute of Technology;Stanford University;Princeton University;Stanford University;Stanford University;Stanford University | Poster | main | Active Perception;Bimanual Manipulation;Imitation Learning;Teleoperation Systems | https://openreview.net/forum?id=1NBdplgILy | -1 | Vision in Action: Learning Active Perception from Human Demonstrations
We present Vision in Action (ViA), an active perception system for bimanual robot manipulation. ViA learns task-relevant active perceptual strategies (e.g., searching, tracking, and focusing) directly from human demonstrations. On the hardware side,... | [
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corl_2025_1TdRe3wPqK | 1TdRe3wPqK | corl | 2,025 | Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence | End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act reliably when faced with out-of-distribution (OOD) visuals induced by objects, backgrounds, or envir... | Pranay Gupta;Henny Admoni;Andrea Bajcsy | Carnegie Mellon University;Carnegie Mellon University; | Poster | main | Visuomotor Policy;Out-of-Distribution Generalization;Functional Correspondence;Deployment-Time Adaptation | https://openreview.net/forum?id=1TdRe3wPqK | -1 | Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence
End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment time, these policies still struggle to act reliabl... | [
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corl_2025_1cA6OYsfoJ | 1cA6OYsfoJ | corl | 2,025 | From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning | Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity.
We propose the first known method enabling robots to autonomously invent relational concepts directly from small sets of unannotated... | Naman Shah;Jayesh Nagpal;Siddharth Srivastava | Allen Institute for Artificial Intelligence+Brown University;; | Poster | main | Learnng symbolic abstractions;Symbolic world model learning;Learning for task and motion planning;learning for planning | https://openreview.net/forum?id=1cA6OYsfoJ | -1 | From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity.
We propose the first k... | [
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corl_2025_1n1Liq6So4 | 1n1Liq6So4 | corl | 2,025 | Meta-Optimization and Program Search using Language Models for Task and Motion Planning | Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. This requirement is formalized in the task and motion planning (TAMP) problem, in which symbolic planning and continuous trajectory generation must be solved in a coordinated manner. Recen... | Denis Shcherba;Eckart Cobo-Briesewitz;Cornelius V. Braun;Marc Toussaint | Technische Universität Berlin;Technische Universität Berlin;Technische Universität Berlin;TU Berlin | Poster | main | Task and Motion Planning;LLMs as Optimizers;Trajectory Optimization | https://openreview.net/forum?id=1n1Liq6So4 | -1 | Meta-Optimization and Program Search using Language Models for Task and Motion Planning
Intelligent interaction with the real world requires robotic agents to jointly reason over high-level plans and low-level controls. This requirement is formalized in the task and motion planning (TAMP) problem, in which symbolic pla... | [
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corl_2025_1otaE496Vm | 1otaE496Vm | corl | 2,025 | CaRL: Learning Scalable Planning Policies with Simple Rewards | We investigate reinforcement learning (RL) for privileged planning in autonomous driving.
State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail.
RL, on the other hand, is scalable and does not suffer from compounding errors like imitation learning.
Contemporary RL ap... | Bernhard Jaeger;Daniel Dauner;Jens Beißwenger;Simon Gerstenecker;Kashyap Chitta;Andreas Geiger | Eberhard-Karls-Universität Tübingen;Eberhard-Karls-Universität Tübingen;Max-Planck-Institute for Intelligent Systems, Max-Planck Institute+Eberhard-Karls-Universität Tübingen;;NVIDIA+University of Tübingen;University of Tuebingen | Poster | main | Autonomous Driving;Reinforcement Learning;Planning | https://openreview.net/forum?id=1otaE496Vm | -1 | CaRL: Learning Scalable Planning Policies with Simple Rewards
We investigate reinforcement learning (RL) for privileged planning in autonomous driving.
State-of-the-art approaches for this task are rule-based, but these methods do not scale to the long tail.
RL, on the other hand, is scalable and does not suffer from ... | [
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corl_2025_23FdMTxEh7 | 23FdMTxEh7 | corl | 2,025 | Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning | In this paper, we tackle the problem of learning to play 3v3 multi-drone volleyball, a new embodied competitive task that requires both high-level strategic coordination and low-level agile control. The task is turn-based, multi-agent, and physically grounded, posing significant challenges due to its long-horizon depen... | Ruize Zhang;Sirui Xiang;Zelai Xu;Feng Gao;Shilong Ji;Wenhao Tang;Wenbo Ding;Chao Yu;Yu Wang | Tsinghua University;Tsinghua University;Tsinghua University;IIIS, Tsinghua University;Tsinghua University;Tsinghua University;Tsinghua Univeresity;Tsinghua University;Tsinghua University | Poster | main | Multi-Agent;Reinforcement Learning;Self-play;Drone Volleyball | https://openreview.net/forum?id=23FdMTxEh7 | -1 | Mastering Multi-Drone Volleyball through Hierarchical Co-Self-Play Reinforcement Learning
In this paper, we tackle the problem of learning to play 3v3 multi-drone volleyball, a new embodied competitive task that requires both high-level strategic coordination and low-level agile control. The task is turn-based, multi-a... | [
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corl_2025_2CIKnIwSta | 2CIKnIwSta | corl | 2,025 | Rapid Mismatch Estimation via Neural Network Informed Variational Inference | With robots increasingly operating in human-centric environments, ensuring soft and safe physical interactions, whether with humans, surroundings, or other machines, is essential. While compliant hardware can facilitate such interactions, this work focuses on impedance controllers that allow torque-controlled robots to... | Mateusz Jaszczuk;Nadia Figueroa | University of Pennsylvania;University of Pennsylvania | Poster | main | Passive Impedance Control;Learning Residual Inverse Dynamics;Model Mismatch Estimation | https://openreview.net/forum?id=2CIKnIwSta | -1 | Rapid Mismatch Estimation via Neural Network Informed Variational Inference
With robots increasingly operating in human-centric environments, ensuring soft and safe physical interactions, whether with humans, surroundings, or other machines, is essential. While compliant hardware can facilitate such interactions, this ... | [
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corl_2025_2dXMfk3qRU | 2dXMfk3qRU | corl | 2,025 | First Order Model-Based RL through Decoupled Backpropagation | There is growing interest in reinforcement learning (RL) methods that leverage the simulator's derivatives to improve learning efficiency. While early gradient-based approaches have demonstrated superior performance compared to derivative-free methods, accessing simulator gradients is often impractical due to their imp... | Joseph Amigo;Rooholla Khorrambakht;Elliot Chane-Sane;Nicolas Mansard;Ludovic Righetti | New York University;New York University;LAAS / CNRS;LAAS / CNRS;New York University+Max-Planck Institute | Poster | main | Model-Based Reinforcement Learning;Quadruped Locomotion;Sim-to-Real Transfer | https://openreview.net/forum?id=2dXMfk3qRU | -1 | First Order Model-Based RL through Decoupled Backpropagation
There is growing interest in reinforcement learning (RL) methods that leverage the simulator's derivatives to improve learning efficiency. While early gradient-based approaches have demonstrated superior performance compared to derivative-free methods, access... | [
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corl_2025_2xvxn3Hm3n | 2xvxn3Hm3n | corl | 2,025 | NeuralSVCD for Efficient Swept Volume Collision Detection | Robot manipulation in unstructured environments requires efficient and reliable Swept Volume Collision Detection (SVCD) for safe motion planning. Traditional discrete methods potentially miss collisions between these points, whereas SVCD continuously checks for collisions along the entire trajectory. Existing SVCD meth... | Hojin Jung;Dongwon Son;Beomjoon Kim | Korea Advanced Institute of Science & Technology;KAIST;Korea Advanced Institute of Science & Technology | Poster | main | Neural swept-volume collision detection;Motion planning | https://openreview.net/forum?id=2xvxn3Hm3n | -1 | NeuralSVCD for Efficient Swept Volume Collision Detection
Robot manipulation in unstructured environments requires efficient and reliable Swept Volume Collision Detection (SVCD) for safe motion planning. Traditional discrete methods potentially miss collisions between these points, whereas SVCD continuously checks for ... | [
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corl_2025_2y7TSgwqAB | 2y7TSgwqAB | corl | 2,025 | GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions | We focus on the task of identifying the location of target regions from a natural language instruction and a front camera image captured by a mobility.
This task is challenging because it requires both existence prediction and segmentation mask generation, particularly for stuff-type target regions with ambiguous bound... | Kei Katsumata;Yui Iioka;Naoki Hosomi;Teruhisa Misu;Kentaro Yamada;Komei Sugiura | Keio University;Keio University, Tokyo Institute of Technology;Honda R&D Co., Ltd.;Honda Research Institute USA, Inc.;Honda;Keio University | Poster | main | Autonomous driving;Vision and Language;Semantic Understanding | https://openreview.net/forum?id=2y7TSgwqAB | -1 | GENNAV: Polygon Mask Generation for Generalized Referring Navigable Regions
We focus on the task of identifying the location of target regions from a natural language instruction and a front camera image captured by a mobility.
This task is challenging because it requires both existence prediction and segmentation mask... | [
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corl_2025_3CnxNqmklv | 3CnxNqmklv | corl | 2,025 | DreamGen: Unlocking Generalization in Robot Learning through Video World Models | In this work, we unlock new capabilities in robot learning from neural trajectories, synthetic robot data generated from video world models. Our proposed recipe is simple, but powerful: we take the most recent state-of-the-art video generative models (world models), adapt them to the target robot embodiment, and genera... | Joel Jang;Seonghyeon Ye;Zongyu Lin;Jiannan Xiang;Johan Bjorck;Yu Fang;Fengyuan Hu;Spencer Huang;Kaushil Kundalia;Yen-Chen Lin;Loïc Magne;Ajay Mandlekar;Avnish Narayan;You Liang Tan;Guanzhi Wang;Jing Wang;Qi Wang;Yinzhen Xu;Xiaohui Zeng;Kaiyuan Zheng;Ruijie Zheng;Ming-Yu Liu;Luke Zettlemoyer;Dieter Fox;Jan Kautz;Scott R... | Department of Computer Science, University of Washington;Korea Advanced Institute of Science & Technology;University of California, Los Angeles;University of California, San Diego;Microsoft;;;;NVIDIA;Massachusetts Institute of Technology;;NVIDIA;;NVIDIA;California Institute of Technology;Nanyang Technological Universit... | Poster | main | Video World Models;Synthetic Data;Behavior Generalization;Environment Generalization | https://openreview.net/forum?id=3CnxNqmklv | -1 | DreamGen: Unlocking Generalization in Robot Learning through Video World Models
In this work, we unlock new capabilities in robot learning from neural trajectories, synthetic robot data generated from video world models. Our proposed recipe is simple, but powerful: we take the most recent state-of-the-art video generat... | [
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corl_2025_3p7rTnLJM8 | 3p7rTnLJM8 | corl | 2,025 | Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation | We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking data to train real-world robot systems. At the core of Lucid-XR is vuer, a web-based physics simulation environment that runs directly on the XR headset, enabling internet-scale access to immersive, latency-free virtual interacti... | Yajvan Ravan;Adam Rashid;Alan Yu;Kai McClennen;Gio Huh;Kevin Yang;Zhutian Yang;Qinxi Yu;Xiaolong Wang;Phillip Isola;Ge Yang | Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;Massachusetts Institute of Technology;California Institute of Technology;;Google;University of Illinois, Urbana Champaign;University of California, San Diego;Massachusetts Institute of Technology;Massachuse... | Poster | main | mixed reality;extended reality;robot manipulation;simulation;mujoco | https://openreview.net/forum?id=3p7rTnLJM8 | -1 | Lucid-XR: An Extended-Reality Data Engine for Robotic Manipulation
We introduce Lucid-XR, a generative data engine for creating diverse and realistic-looking data to train real-world robot systems. At the core of Lucid-XR is vuer, a web-based physics simulation environment that runs directly on the XR headset, enabling... | [
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corl_2025_4IuTfpWGDR | 4IuTfpWGDR | corl | 2,025 | TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization | Robots often struggle to generalize from a single demonstration due to the lack of a transferable and interpretable spatial representation. In this work, we introduce TReF-6, a method that infers a simplified, abstracted 6DoF Task-Relevant Frame from a single trajectory. Our approach identifies an influence point purel... | Yuxuan Ding;Shuangge Wang;Tesca Fitzgerald | ;Yale University;Yale University | Poster | main | Spatial Reference Frames;One-Shot Imitation Learning;Dynamic Movement Primitives | https://openreview.net/forum?id=4IuTfpWGDR | -1 | TReF-6: Inferring Task-Relevant Frames from a Single Demonstration for One-Shot Skill Generalization
Robots often struggle to generalize from a single demonstration due to the lack of a transferable and interpretable spatial representation. In this work, we introduce TReF-6, a method that infers a simplified, abstracte... | [
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corl_2025_4Po2mqLjrQ | 4Po2mqLjrQ | corl | 2,025 | Motion Blender Gaussian Splatting for Dynamic Reconstruction | Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per-Gaussian parameters, which makes it difficult to further manipulate the reconstructed m... | Xinyu Zhang;Haonan Chang;Yuhan Liu;Abdeslam Boularias | Rutgers University;;Rutgers University;, Rutgers University | Poster | main | Dynamic Reconstruction;Gaussian Splatting | https://openreview.net/forum?id=4Po2mqLjrQ | -1 | Motion Blender Gaussian Splatting for Dynamic Reconstruction
Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per-Gaussian parameters, which... | [
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corl_2025_4XKKUifQ9c | 4XKKUifQ9c | corl | 2,025 | ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes | Dexterous grasping in cluttered scenes presents significant challenges due to diverse object geometries, occlusions, and potential collisions. Existing methods primarily focus on single-object grasping or grasp-pose prediction without interaction, which are insufficient for complex, cluttered scenes. Recent vision-lang... | Zeyuan Chen;Qiyang Yan;Yuanpei Chen;Tianhao Wu;Jiyao Zhang;Zihan Ding;Jinzhou Li;Yaodong Yang;Hao Dong | AgiBot+Peking University;AgiBot;PsiRobot;Peking University;Peking University;Princeton University;Duke University+Peking University;;Peking University+Peking University | Oral | main | Cluttered Scene;Dexterous Grasping;Sim-to-Real | https://openreview.net/forum?id=4XKKUifQ9c | -1 | ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
Dexterous grasping in cluttered scenes presents significant challenges due to diverse object geometries, occlusions, and potential collisions. Existing methods primarily focus on single-object grasping or grasp-pose prediction with... | [
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corl_2025_4eMWCoWUKR | 4eMWCoWUKR | corl | 2,025 | Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering | As robots become increasingly capable of operating over extended periods—spanning days, weeks, and even months—they are expected to accumulate knowledge of their environments and leverage this experience to assist humans more effectively. This paper studies the problem of Long-term Active Embodied Question Answering (L... | Muhammad Fadhil Ginting;Dong-Ki Kim;Xiangyun Meng;Andrzej Marek Reinke;Bandi Jai Krishna;Navid Kayhani;Oriana Peltzer;David Fan;Amirreza Shaban;Sung-Kyun Kim;Mykel Kochenderfer;Ali-akbar Agha-mohammadi;Shayegan Omidshafiei | Stanford University;Field AI;University of Washington;;;Field AI;;Jet Propulsion Laboratory;University of Washington, Seattle;;Stanford University;;FieldAI | Poster | main | embodied question answering;long-term reasoning;vision-language navigation | https://openreview.net/forum?id=4eMWCoWUKR | -1 | Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
As robots become increasingly capable of operating over extended periods—spanning days, weeks, and even months—they are expected to accumulate knowledge of their environments and leverage this experience to assist humans more... | [
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corl_2025_4eSv0QeYlz | 4eSv0QeYlz | corl | 2,025 | Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments | Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environment knowledge and are typically too slow for dynamic scenes. Neural motion policies offer a pro... | Jiahui Yang;Jason Jingzhou Liu;Yulong Li;Youssef Khaky;Kenneth Shaw;Deepak Pathak | Carnegie Mellon University;;Massachusetts Institute of Technology;Carnegie Mellon University;Carnegie Mellon University;Skild AI+Carnegie Mellon University | Poster | main | Motion Planning;Visuo-Motor Policy;Reactive Control | https://openreview.net/forum?id=4eSv0QeYlz | -1 | Deep Reactive Policy: Learning Reactive Manipulator Motion Planning for Dynamic Environments
Generating collision-free motion in dynamic, partially observable environments is a fundamental challenge for robotic manipulators. Classical motion planners can compute globally optimal trajectories but require full environmen... | [
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corl_2025_5htQM8jqOe | 5htQM8jqOe | corl | 2,025 | D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation | Mastering deformable object manipulation often necessitates the use of anthropomorphic, high-degree-of-freedom robot hands capable of precise, contact-rich control. However, current trajectory optimisation methods often struggle in these settings due to the large search space and the sparse task information available f... | Jun Yamada;Shaohong Zhong;Jack Collins;Ingmar Posner | University of Oxford;University of Oxford;University of Oxford;Amazon+University of Oxford | Poster | main | Trajectory Optimisation;Dexterous Deformable Object Manipulation;Latent Diffusion Model;Gradient-Free Guidance | https://openreview.net/forum?id=5htQM8jqOe | -1 | D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
Mastering deformable object manipulation often necessitates the use of anthropomorphic, high-degree-of-freedom robot hands capable of precise, contact-rich control. However, current trajectory optimisation methods often struggle in ... | [
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... | ||
corl_2025_5ySSVlJBOn | 5ySSVlJBOn | corl | 2,025 | FetchBot: Learning Generalizable Object Fetching in Cluttered Scenes via Zero-Shot Sim2Real | Generalizable object fetching in cluttered scenes remains a fundamental and application-critical challenge in embodied AI. Closely packed objects cause inevitable occlusions, making safe action generation particularly difficult. Under such partial observability, effective policies must not only generalize across divers... | Weiheng Liu;Yuxuan Wan;Jilong Wang;Yuxuan Kuang;Xuesong Shi;Haoran Li;Dongbin Zhao;Zhizheng Zhang;He Wang | Institute of Automation, Chinese Academy of Sciences;Peking University;Galbot Co. Ltd.;School of Computer Science, Carnegie Mellon University+Peking University;Galbot;Institute of Automation, Chinese Academy of Sciences;Institute of Automation, Chinese Academy of Sciences;Beijing Galbot Co., Ltd;Galbot+Peking Universit... | Oral | main | Generalizable Fetching;Sim2Real;Occlusion Handling | https://openreview.net/forum?id=5ySSVlJBOn | -1 | FetchBot: Learning Generalizable Object Fetching in Cluttered Scenes via Zero-Shot Sim2Real
Generalizable object fetching in cluttered scenes remains a fundamental and application-critical challenge in embodied AI. Closely packed objects cause inevitable occlusions, making safe action generation particularly difficult.... | [
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... | ||
corl_2025_6AASPlloSt | 6AASPlloSt | corl | 2,025 | RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models | Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out of the box on new tasks in new environments. However, for such models to be truly useful, an end user must have easy means to teach them to... | Kaustubh Sridhar;Souradeep Dutta;Dinesh Jayaraman;Insup Lee | Google Deepmind+University of Pennsylvania;University of British Columbia;University of Pennsylvania;University of Pennsylvania | Poster | main | Vision-Language-Action (VLA) models;In-Context Learning (ICL);Retrieval-Augmented Generation (RAG) | https://openreview.net/forum?id=6AASPlloSt | -1 | RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models
Multi-task ``vision-language-action'' (VLA) models have recently demonstrated increasing promise as generalist foundation models for robotics, achieving non-trivial performance out of the box on new tasks in new environments. However, for... | [
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0.008887982927262783,
0.0... | ||
corl_2025_6yB6AX8aSU | 6yB6AX8aSU | corl | 2,025 | LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations | Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity and seamless sequencing of manipulation skills while robustly handling environment variations. While imitation learning offers a promising app... | Weikang Wan;Jiawei Fu;Xiaodi Yuan;Yifeng Zhu;Hao Su | University of California, San Diego;University of California, San Diego;University of California, San Diego;The University of Texas at Austin;University of California, San Diego | Poster | main | Dexterous Manipulation;Imitation Learning;Sim-to-Real | https://openreview.net/forum?id=6yB6AX8aSU | -1 | LodeStar: Long-horizon Dexterity via Synthetic Data Augmentation from Human Demonstrations
Developing robotic systems capable of robustly executing long-horizon manipulation tasks with human-level dexterity is challenging, as such tasks require both physical dexterity and seamless sequencing of manipulation skills whil... | [
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corl_2025_7OOMC7pzaw | 7OOMC7pzaw | corl | 2,025 | TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types | Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand retargeting to closely mimic human hand postures. However, these approaches may fail to fully leverage the inherent dexterity of dexterous ... | Yuhao Lin;Yi-Lin Wei;Haoran Liao;Mu Lin;Chengyi Xing;Hao Li;Dandan Zhang;Mark Cutkosky;Wei-Shi Zheng | SUN YAT-SEN UNIVERSITY;;;SUN YAT-SEN UNIVERSITY;Stanford University;Stanford University;Imperial College London;Stanford University;SUN YAT-SEN UNIVERSITY | Poster | main | Teleoperation;Dexterous;Manipulation | https://openreview.net/forum?id=7OOMC7pzaw | -1 | TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
Dexterous teleoperation plays a crucial role in robotic manipulation for real-world data collection and remote robot control. Previous dexterous teleoperation mostly relies on hand retargeting to closely mimic human hand postures. However, t... | [
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corl_2025_7XyO9Y1hI1 | 7XyO9Y1hI1 | corl | 2,025 | EndoVLA: Dual-Phase Vision-Language-Action for Precise Autonomous Tracking in Endoscopy | In endoscopic procedures, autonomous tracking of abnormal regions and following of circumferential cutting markers can significantly reduce the cognitive burden on endoscopists. However, conventional model-based pipelines are fragile—each component (e.g., detection, motion planning) requires manual tuning and struggles... | CHI KIT NG;Long Bai;Guankun Wang;Yupeng Wang;Huxin Gao;Kun yuan;Chenhan Jin;Tieyong Zeng;Hongliang Ren | Chinese University of Hong Kong;;The Chinese University of Hong Kong;The Chinese University of Hong Kong;The Chinese University of Hong Kong;Université de Strasbourg+Technische Universität München;;The Chinese University of Hong Kong;The Chinese University of Hong Kong | Poster | main | Vision–Language–Action;Continuum Robots;Autonomous Endoscopic Tracking;Reinforcement Learning | https://openreview.net/forum?id=7XyO9Y1hI1 | -1 | EndoVLA: Dual-Phase Vision-Language-Action for Precise Autonomous Tracking in Endoscopy
In endoscopic procedures, autonomous tracking of abnormal regions and following of circumferential cutting markers can significantly reduce the cognitive burden on endoscopists. However, conventional model-based pipelines are fragil... | [
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0.0265... | ||
corl_2025_7iaYcss56y | 7iaYcss56y | corl | 2,025 | ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation | Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, and physical aspects hinder direct imitation. To effectively bridge the domain gap, we propose ImMimic, an embodiment-agnostic co-training fra... | Yangcen Liu;Woo Chul Shin;Yunhai Han;Zhenyang Chen;Harish Ravichandar;Danfei Xu | Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;;Georgia Institute of Technology;Georgia Institute of Technology+NVIDIA | Oral | main | Learning from Human;Imitation learning;Dexterous Manipulation | https://openreview.net/forum?id=7iaYcss56y | -1 | ImMimic: Cross-Domain Imitation from Human Videos via Mapping and Interpolation
Learning robot manipulation from abundant human videos offers a scalable alternative to costly robot-specific data collection. However, domain gaps across visual, morphological, and physical aspects hinder direct imitation. To effectively b... | [
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... | ||
corl_2025_7wGYX11BJB | 7wGYX11BJB | corl | 2,025 | ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation | 3D world models (i.e., learning-based 3D dynamics models) offer a promising approach to generalizable robotic manipulation by capturing the underlying physics of environment evolution conditioned on robot actions. However, existing 3D world models are primarily limited to single-material dynamics using a particle-based... | Suning Huang;Qianzhong Chen;Xiaohan Zhang;Jiankai Sun;Mac Schwager | ;Stanford University;Boston Dynamics AI Institute;Stanford University;Stanford University | Poster | main | Learning-based Dynamics Modeling;Model-based Planning | https://openreview.net/forum?id=7wGYX11BJB | -1 | ParticleFormer: A 3D Point Cloud World Model for Multi-Object, Multi-Material Robotic Manipulation
3D world models (i.e., learning-based 3D dynamics models) offer a promising approach to generalizable robotic manipulation by capturing the underlying physics of environment evolution conditioned on robot actions. However... | [
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0.0298960823565721... | ||
corl_2025_8DHSyMFLbB | 8DHSyMFLbB | corl | 2,025 | Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids | Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (... | Toru Lin;Kartik Sachdev;Linxi Fan;Jitendra Malik;Yuke Zhu | ;NVIDIA;NVIDIA;Meta Facebook+University of California, Berkeley;Computer Science Department, University of Texas, Austin | Poster | main | Humanoids;Vision-Based Dexterous Manipulation;Reinforcement Learning;Sim-to-Real | https://openreview.net/forum?id=8DHSyMFLbB | -1 | Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are ... | [
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... | ||
corl_2025_8RdxHk9hpr | 8RdxHk9hpr | corl | 2,025 | Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation | Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This conservative approach severely underutilizes the robot's inherent capabilities---our analysis demonstrates that manipulators can safely handle p... | Anuj Pasricha;Joewie J. Koh;Jay Vakil;Alessandro Roncone | University of Colorado at Boulder;;University of Colorado at Boulder;University of Colorado at Boulder | Poster | main | Robot Planning;Grasping & Manipulation;Robot Modeling & Simulation;diffusion models;dynamics-constrained planning;payload transport | https://openreview.net/forum?id=8RdxHk9hpr | -1 | Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation
Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This conservative approach severely underutilizes the robot's inherent... | [
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0.0... | ||
corl_2025_8v0mlyKk5q | 8v0mlyKk5q | corl | 2,025 | Beyond Constant Parameters: Hyper Prediction Models and HyperMPC | Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complexity and state representation. To address this limitation, we prop... | Jan Węgrzynowski;Piotr Kicki;Grzegorz Czechmanowski;Maciej Piotr Krupka;Krzysztof Walas | Technical University of Poznan;Technical University of Poznan+IDEAS NCBR Sp.;;;Technical University of Poznan | Poster | main | Model Learning for Robot Control;Dynamics Model Learning;Model Predictive Control;MPC | https://openreview.net/forum?id=8v0mlyKk5q | -1 | Beyond Constant Parameters: Hyper Prediction Models and HyperMPC
Model Predictive Control (MPC) is among the most widely adopted and reliable methods for robot control, relying critically on an accurate dynamics model. However, existing dynamics models used in the gradient-based MPC are limited by computational complex... | [
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0.0... | ||
corl_2025_93bWCbhXJR | 93bWCbhXJR | corl | 2,025 | Distilling On-device Language Models for Robot Planning with Minimal Human Intervention | Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or industrial settings. ... | Zachary Ravichandran;Ignacio Hounie;Fernando Cladera;Alejandro Ribeiro;George J. Pappas;Vijay Kumar | University of Pennsylvania;University of Pennsylvania;University of Pennsylvania;University of Pennsylvania;; | Poster | main | LLM-enabled Robots;LLM Distillation | https://openreview.net/forum?id=93bWCbhXJR | -1 | Distilling On-device Language Models for Robot Planning with Minimal Human Intervention
Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environment... | [
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0... | ||
corl_2025_9AHjtHLlIe | 9AHjtHLlIe | corl | 2,025 | Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models | Exploration is key for general-purpose robotic learning, particularly in open-ended environments where explicit guidance or task-specific feedback is limited. Vision-language models (VLMs), which can reason about object semantics, spatial relations, and potential outcomes, offer a promising foundation for guiding explo... | Seungjae Lee;Daniel Ekpo;Haowen Liu;Furong Huang;Abhinav Shrivastava;Jia-Bin Huang | University of Maryland, College Park;University of Maryland, College Park;;University of Maryland;Department of Computer Science, University of Maryland, College Park;University of Maryland, College Park | Poster | main | Exploration;Agentic System;Vision-Language Model | https://openreview.net/forum?id=9AHjtHLlIe | -1 | Imagine, Verify, Execute: Memory-guided Agentic Exploration with Vision-Language Models
Exploration is key for general-purpose robotic learning, particularly in open-ended environments where explicit guidance or task-specific feedback is limited. Vision-language models (VLMs), which can reason about object semantics, s... | [
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... | ||
corl_2025_9FpccnRarn | 9FpccnRarn | corl | 2,025 | BEVCalib: LiDAR-Camera Calibration via Geometry-Guided Bird’s-Eye View Representation | Accurate LiDAR-camera calibration is the foundation of accurate multimodal fusion environmental perception for autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled environments and cannot compensate for the transformation changes during the vehicle/robo... | Weiduo Yuan;Jerry Li;Justin Yue;Divyank Shah;Konstantinos Karydis;Hang Qiu | University of Southern California;University of Southern California+University of California, Riverside;University of California, Riverside;University of California, Riverside;;University of California, Riverside | Poster | main | LiDAR-Camera Calibration;Autonomous Driving;BEV Features | https://openreview.net/forum?id=9FpccnRarn | -1 | BEVCalib: LiDAR-Camera Calibration via Geometry-Guided Bird’s-Eye View Representation
Accurate LiDAR-camera calibration is the foundation of accurate multimodal fusion environmental perception for autonomous driving and robotic systems. Traditional calibration methods require extensive data collection in controlled env... | [
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... |
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