机器人 / 具身智能

Imitation Learning for Generalizable Self-driving Policy with Sim-to-real Transfer

Zoltán Lőrincz, Márton Szemenyei, Róbert Moni

专题命中 机器人数据与评测 ：robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments Accepted by ICLR 2022 Workshop on Generalizable Policy Learning in Physical World. Source code is available at: https://github.com/lzoltan35/duckietown_imitation_learning

MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

Vittorio Caggiano, Huawei Wang, Guillaume Durandau, Massimo Sartori, Vikash Kumar

专题命中 机器人数据与评测 ：robotics(abstract)；embodied agent(abstract)；分类 cs.RO、cs.AI、cs.LG

Policy Architectures for Compositional Generalization in Control

Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran

专题命中 机器人数据与评测 ：robotics(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

Omnipush: accurate, diverse, real-world dataset of pushing dynamics with RGB-D video

Maria Bauza, Ferran Alet, Yen-Chen Lin, Tomas Lozano-Perez, Leslie P. Kaelbling, Phillip Isola, Alberto Rodriguez

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments IROS 2019, 8 pages, 7 figures

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments This is an update version of a manuscript that originally appeared at CoRL 2019. Videos are here: meta-world.github.io, open-sourced code are available at: https://github.com/rlworkgroup/metaworld, and the baselines can be found at https://github.com/rlworkgroup/garage

Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics

Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, Ashwin Balakrishna, Daniel Seita, Jennifer Grannen, Minho Hwang, Ryan Hoque, Joseph E. Gonzalez, Nawid Jamali, Katsu Yamane, Soshi Iba, Ken Goldberg

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video with Applications for Virtual Reality

Alisha Sharma, Ryan Nett, Jonathan Ventura

专题命中 机器人数据与评测 ：navigation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments Expansion on arXiv:1901.00979 for IJSC SI; correct table 1 and 3 headings, reduce file size

Journal ref Int.J.Semantic Computing 14(3) (2020) 315-322

Predicting Sim-to-Real Transfer with Probabilistic Dynamics Models

Lei M. Zhang, Matthias Plappert, Wojciech Zaremba

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Unsupervised Learning of Depth and Ego-Motion from Cylindrical Panoramic Video

Alisha Sharma, Jonathan Ventura

专题命中 机器人数据与评测 ：navigation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments Accepted to IEEE AIVR 2019

KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects

Xingyu Liu, Rico Jonschkowski, Anelia Angelova, Kurt Konolige

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments CVPR 2020

Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures

Johannes Günther, Nadia M. Ady, Alex Kearney, Michael R. Dawson, Patrick M. Pilarski

专题命中 机器人数据与评测 ：robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Online Object Representations with Contrastive Learning

Sören Pirk, Mohi Khansari, Yunfei Bai, Corey Lynch, Pierre Sermanet

专题命中 机器人数据与评测 ：robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments 10 pages

Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Yunzhu Li, Jiajun Wu, Russ Tedrake, Joshua B. Tenenbaum, Antonio Torralba

专题命中 机器人数据与评测 ：robot learning(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments Accepted to ICLR 2019. Project Page: http://dpi.csail.mit.edu Video: https://www.youtube.com/watch?v=FrPpP7aW3Lg

The Probabilistic Object Detection Challenge

John Skinner, David Hall, Haoyang Zhang, Feras Dayoub, Niko Sünderhauf

专题命中 机器人数据与评测 ：robotics(abstract)；robotic(abstract)；分类 cs.RO、cs.CV、cs.LG

Comments 4 pages, workshop paper

Fast Model Identification via Physics Engines for Data-Efficient Policy Search

Shaojun Zhu, Andrew Kimmel, Kostas E. Bekris, Abdeslam Boularias

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments IJCAI 18

HoME: a Household Multimodal Environment

Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron Courville

专题命中 机器人数据与评测 ：robotics(abstract)；navigation(abstract)；分类 cs.RO、cs.AI、cs.CV

Comments Presented at NIPS 2017's Visually-Grounded Interaction and Language Workshop

Learning from the Hindsight Plan -- Episodic MPC Improvement

Aviv Tamar, Garrett Thomas, Tianhao Zhang, Sergey Levine, Pieter Abbeel

专题命中 机器人数据与评测 ：robotics(abstract)；manipulation(abstract)；分类 cs.RO、cs.AI、cs.LG

Comments Additional experiments for neural network generalization and for varying the planning horizon. Paper accepted to ICRA 2017

RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies

RoboLab：一种高保真模拟基准，用于分析任务通用策略

Jenai Xuning Yang, Rishit Dagli, Alex Zook, Hugo Hadfield, Ankit Goyal, Stan Birchfield, Fabio Ramos, Jonathan Tremblay

机构 * NVIDIA ； University of Toronto（多伦多大学） ； The University of Sydney（悉尼大学） 纠错

专题命中 机器人数据与评测 ：robotics(abstract,journal_ref)；robotic(abstract)；分类 cs.RO、cs.AI

AI总结 RoboLab通过高保真模拟环境评估任务通用策略的真实泛化能力，提供120个任务的视觉、程序和关系能力测试，揭示当前最佳模型的性能差距。

Journal ref Robotics: Science and Systems XXII, Sydney, Australia, 2026

Lightweight 3D Object Detection via Mamba-Based Knowledge Distillation

基于Mamba知识蒸馏的轻量型3D目标检测

Quoc Cuong Ninh, Huy Xuan Pham, Anh Tung Nguyen, Dinh Hoan Trinh

专题命中 机器人数据与评测 ：navigation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV；robotics(comments)

AI总结 本研究提出基于Mamba的知识蒸馏框架，通过选择性体素空间特征对齐实现轻量型3D目标检测，在保持精度的同时显著降低计算负载。

Comments Accepted for publication in IEEE Robotics and Automation Letters (RA-L), 2026

3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance

3D HAMSTER：通过3D轨迹引导在分层视觉语言动作模型中桥接规划与控制

Dongyoon Hwang, Byungkun Lee, Dongjin Kim, Hyojin Jang, Hoiyeong Jin, Jueun Mun, Minho Park, Hojoon Lee, Hyunseung Kim, Jaegul Choo

机构 * Kim Jaechul Graduate School of AI, KAIST（韩国科学技术院金载哲人工智能研究生院） ； Graduate School of Artificial Intelligence, POSTECH（浦项工业大学人工智能研究生院） ； KRAFTON AI 纠错

专题命中 机器人数据与评测 ：robotics(abstract,comments)；manipulation(abstract)；分类 cs.RO、cs.AI

AI总结 提出3D HAMSTER框架，通过让规划器直接输出度量可靠的3D轨迹，弥合2D引导与3D低层策略之间的差距，在3D轨迹预测、仿真和真实操作中优于现有方法。

Comments Published in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2026. Code: https://github.com/DAVIAN-Robotics/3D_HAMSTER. Project page: https://davian-robotics.github.io/3D_HAMSTER/

CLARE: Continual Learning for Vision-Language-Action Models via Autonomous Adapter Routing and Expansion

CLARE：通过自主适配器路由和扩展实现视觉-语言-动作模型的持续学习

Ralf Römer, Yi Zhang, Yuming Li, Angela P. Schoellig

机构 * Technical University of Munich（慕尼黑技术大学） ； TUM School of Computation, Information and Technology（TUM计算、信息与技术学院） ； Department of Computer Engineering, Learning Systems and Robotics Lab（计算机工程系、学习系统与机器人实验室） ； Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑机器人与机器智能研究所（MIRMI）） ； Robotics Institute Germany（德国机器人研究所） ； Munich Center for Machine Learning（慕尼黑机器学习中心） 纠错

专题命中 机器人数据与评测 ：robotics(abstract,comments)；manipulation(abstract)；分类 cs.RO、cs.LG

AI总结 CLARE提出一种参数高效、无需示例的持续学习框架，通过自主扩展模型模块，实现机器人在新任务中保持旧知识，优于基于示例的方法。

Comments Accepted to IEEE Robotics and Automation Letters 2026. Project page: https://tum-lsy.github.io/clare. 11 pages, 9 figures

Moving On, Even When You're Broken: Fail-Active Trajectory Generation via Diffusion Policies Conditioned on Embodiment and Task

继续前行，即使你已破损：通过扩散策略条件于躯体与任务的失败主动轨迹生成

Gilberto G. Briscoe-Martinez, Yaashia Gautam, Rahul Shetty, Anuj Pasricha, Marco M. Nicotra, Alessandro Roncone

机构 * Dept. of Computer Science, University of Colorado Boulder, CO, USA（计算机科学系，科罗拉多大学博尔德分校） ； Dept. of Electrical, Computer, and Energy Engineering, University of Colorado Boulder, CO, USA（电气、计算机与能源工程系，科罗拉多大学博尔德分校） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.AI；robotics(comments)

AI总结 DEFT通过扩散策略实现机器人在受损情况下完成任务，其在多种失败条件下表现出高成功率和鲁棒性。

Comments To be published in the 2026 IEEE International Conference on Robotics & Automation

cVLA: Towards Efficient Camera-Space VLAs

cVLA：迈向高效的相机空间VLA

Max Argus, Jelena Bratulic, Houman Masnavi, Maxim Velikanov, Nick Heppert, Abhinav Valada, Thomas Brox

机构 * University of Freiburg（弗赖堡大学） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract,comments)；robotic(abstract)；分类 cs.RO、cs.LG

AI总结 cVLA通过利用视觉语言模型在2D图像上的性能，提出了一种高效预测机器人轨迹的VLA方法，具备良好的仿真到现实迁移能力。

Comments 20 pages, 10 figures; CoRL 2025 Workshop on Generalizable Priors for Robot Manipulation

JaxRobotarium: Training and Deploying Multi-Robot Policies in 10 Minutes

Shalin Anand Jain, Jiazhen Liu, Siva Kailas, Harish Ravichandar

机构 * Georgia Institute of Technology（佐治亚理工学院） 纠错

专题命中 机器人数据与评测 ：robot learning(abstract,comments)；robotics(abstract)；分类 cs.RO、cs.LG

Comments 22 pages, 14 figures, 10 tables. https://github.com/GT-STAR-Lab/JaxRobotarium. Manuscript accepted for publication at the 9th Conference on Robot Learning (CoRL 2025), Seoul, Korea

Generating Stable Placements via Physics-guided Diffusion Models

Philippe Nadeau, Miguel Rogel, Ivan Bilić, Ivan Petrović, Jonathan Kelly

机构 * STARS Laboratory, University of Toronto Institute for Aerospace Studies（多伦多大学航空航天研究所STARS实验室） ； Laboratory for Autonomous Systems and Mobile Robotics（自主系统与移动机器人实验室） ； University of Zagreb Faculty of Electrical Engineering and Computing（Zagreb大学电气工程与计算学院） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.LG；robotics(comments)

Comments Submitted to the IEEE International Conference on Robotics and Automation 2026, Vienna, Austria, June 1-5, 2026

Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot

Federico Ceola, Elisa Maiettini, Giulia Pasquale, Giacomo Meanti, Lorenzo Rosasco, Lorenzo Natale

专题命中 机器人数据与评测 ：robotics(abstract,journal_ref)；robotic(abstract)；分类 cs.RO、cs.CV

Comments ©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref IEEE Transactions on Robotics, Volume 38, Issue 5, October 2022

Articulated Object Estimation in the Wild

Abdelrhman Werby, Martin Büchner, Adrian Röfer, Chenguang Huang, Wolfram Burgard, Abhinav Valada

机构 * University of Freiburg（弗赖堡大学） ； University of Stuttgart（斯图加特大学） ； University of Technology Nuremberg（纽伦堡技术大学） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV；robot learning(comments)

Comments 9th Conference on Robot Learning (CoRL), 2025

ROVER: A Multi-Season Dataset for Visual SLAM

Fabian Schmidt, Julian Daubermann, Marcel Mitschke, Constantin Blessing, Stefan Meyer, Markus Enzweiler, Abhinav Valada

机构 * Institute for Intelligent Systems, Esslingen University of Applied Sciences（智能系统研究所，埃斯林根应用科学大学） ； Department of Computer Science, University of Freiburg（计算机科学系，弗赖堡大学） ； ANDREAS STIHL AG & Co. KG（安德烈亚斯·施塔尔有限公司） 纠错

专题命中 机器人数据与评测 ：navigation(abstract)；robotic(abstract)；分类 cs.RO、cs.CV；robotics(journal_ref)

Comments Copyright 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Journal ref IEEE Transactions on Robotics, vol. 41, pp. 4005-4022, 2025

Demonstrating Multi-Suction Item Picking at Scale via Multi-Modal Learning of Pick Success

Che Wang, Jeroen van Baar, Chaitanya Mitash, Shuai Li, Dylan Randle, Weiyao Wang, Sumedh Sontakke, Kostas E. Bekris, Kapil Katyal

机构 * Amazon Robotics（亚马逊机器人技术） 纠错

专题命中 机器人数据与评测 ：manipulation(abstract)；robotic(abstract)；分类 cs.RO、cs.LG；robotics(comments)

Comments Accepted to Robotics: Science and Systems (RSS 2025), 15 pages

Domain Randomization for Object Detection in Manufacturing Applications using Synthetic Data: A Comprehensive Study

Xiaomeng Zhu, Jacob Henningsson, Duruo Li, Pär Mårtensson, Lars Hanson, Mårten Björkman, Atsuto Maki

机构 * Scania CV AB（斯堪尼亚公司） ； Skövde University（斯德哥尔摩大学） ； KTH Royal Institute of Technology（皇家理工学院） 纠错

专题命中 机器人数据与评测 ：robotics(abstract,comments)；robotic(abstract)；分类 cs.AI、cs.CV

Comments This is accepted by 2025 IEEE International Conference on Robotics & Automation (ICRA), waiting for publication. 14 pages, 14 figures