CoRL

Control with Patterns: A D-learning Method

基于模式的控制：一种D学习方法

Quan Quan, Kai-Yuan Cai, Chenyu Wang

机构 * Beihang University（北京航空航天大学） 纠错

AI总结 本文提出了一种基于模式的控制方法，利用D学习在无需系统动力学知识的情况下解决非线性动力系统稳定性问题，并通过模拟和实际飞行实验验证其有效性。

Comments Accepted for publication at 8th Conference on Robot Learning (CoRL), Munich, Germany. 2024

Journal ref In 8th Annual Conference on Robot Learning, Munich,2024:270

Off Policy Lyapunov Stability in Reinforcement Learning

强化学习中的非策略李雅普诺夫稳定性

Sarvan Gill, Daniela Constantinescu

机构 * Department of Mechanical Engineering University of Victoria, Canada（机械工程系维多利亚大学） 纠错

AI总结 本文提出非策略李雅普诺夫函数学习方法，将其应用于软演员批评者和近端策略优化算法，提升其数据效率和稳定性保证。

Comments Conference on Robot Learning (CORL) 2025

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

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

Exposing Vulnerabilities in RL: A Novel Stealthy Backdoor Attack through Reward Poisoning

揭示强化学习中的漏洞：通过奖励污染的新型隐秘后门攻击

Bokang Zhang, Chaojun Lu, Jianhui Li, Junfeng Wu

AI总结 本文提出通过奖励污染对强化学习代理进行隐秘后门攻击，展示了在不同环境中攻击的有效性和隐蔽性。

Comments Workshop on Safe and Robust Robot Learning for Operation in the Real World at CoRL 2025

NOIR 2.0: Neural Signal Operated Intelligent Robots for Everyday Activities

NOIR 2.0：神经信号操作的智能机器人用于日常活动

Tasha Kim, Yingke Wang, Hanvit Cho, Alex Hodges

机构 * Institute for Computational and Mathematical Engineering（计算与数学工程研究所） ； Department of Computer Science（计算机科学系） ； Department of Mechanical Engineering（机械工程系） 纠错

AI总结 NOIR 2.0通过改进的脑解码算法和少样本学习提升机器人执行日常任务的效率和适应性。

Comments Conference on Robot Learning (CoRL 2024), CoRoboLearn

Latent Adaptive Planner for Dynamic Manipulation

动态操作的潜在自适应规划器

Donghun Noh, Deqian Kong, Minglu Zhao, Andrew Lizarraga, Jianwen Xie, Ying Nian Wu, Dennis Hong

机构 * UCLA（加州大学洛杉矶分校） ； Lambda, Inc.（Lambda公司） 纠错

AI总结 LAP通过学习人类示范视频，实现了动态非抓取操作的实时自适应规划，提升操作成功率和效率。

Journal ref Proceedings of The 9th Conference on Robot Learning, PMLR 305:2430-2448, 2025

KoopMotion: Learning Almost Divergence Free Koopman Flow Fields for Motion Planning

Alice Kate Li, Thales C Silva, Victoria Edwards, Vijay Kumar, M. Ani Hsieh

机构 * GRASP Laboratory University of Pennsylvania（GRASP实验室 大学） 纠错

Comments Revised with link to code. Accepted to CoRL 2025 (Conference on Robot Learning). 15 pages 11 figures

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

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

KDPE: A Kernel Density Estimation Strategy for Diffusion Policy Trajectory Selection

Andrea Rosasco, Federico Ceola, Giulia Pasquale, Lorenzo Natale

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

Journal ref 2025 Conference on Robot Learning

Search-TTA: A Multimodal Test-Time Adaptation Framework for Visual Search in the Wild

Derek Ming Siang Tan, Shailesh, Boyang Liu, Alok Raj, Qi Xuan Ang, Weiheng Dai, Tanishq Duhan, Jimmy Chiun, Yuhong Cao, Florian Shkurti, Guillaume Sartoretti

Comments Accepted for presentation at CORL 2025. Code, models, and data are available at https://search-tta.github.io/

Decentralized Aerial Manipulation of a Cable-Suspended Load using Multi-Agent Reinforcement Learning

Jack Zeng, Andreu Matoses Gimenez, Eugene Vinitsky, Javier Alonso-Mora, Sihao Sun

机构 * Delft University of Technology（代尔夫特理工大学） ； NYU Tandon School of Engineering（纽约大学坦顿工程学院） 纠错

Journal ref Proceedings of the 9th Conference on Robot Learning, PMLR 305:3850-3868, 2025

MOSAIC: Modular Foundation Models for Assistive and Interactive Cooking

Huaxiaoyue Wang, Kushal Kedia, Juntao Ren, Rahma Abdullah, Atiksh Bhardwaj, Angela Chao, Kelly Y Chen, Nathaniel Chin, Prithwish Dan, Xinyi Fan, Gonzalo Gonzalez-Pumariega, Aditya Kompella, Maximus Adrian Pace, Yash Sharma, Xiangwan Sun, Neha Sunkara, Sanjiban Choudhury

Comments 22 pages, 13 figures; CoRL 2024

Improving the performance of AI-powered Affordable Robotics for Assistive Tasks

Dharunish Yugeswardeenoo

机构 * Central Bucks High School South（中央巴克斯高中南校区） 纠错

Comments 6 pages, 5 figures. Accepted to Conference on Robot Learning (CoRL 2025), Seoul, Korea

VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning

Binghao Huang, Jie Xu, Iretiayo Akinola, Wei Yang, Balakumar Sundaralingam, Rowland O'Flaherty, Dieter Fox, Xiaolong Wang, Arsalan Mousavian, Yu-Wei Chao, Yunzhu Li

机构 * Columbia University（哥伦比亚大学） ； NVIDIA（英伟达） ； University of California, San Diego（加州大学圣地亚哥分校） 纠错

Comments Accepted by 9th Conference on Robot Learning (CoRL 2025); Website: https://binghao-huang.github.io/vt_refine/

QuaDreamer: Controllable Panoramic Video Generation for Quadruped Robots

Sheng Wu, Fei Teng, Hao Shi, Qi Jiang, Kai Luo, Kaiwei Wang, Kailun Yang

机构 * Hunan University（湖南大学） ； Zhejiang University（浙江大学） ； Nanyang Technological University（南洋理工大学） 纠错

Comments Accepted to CoRL 2025. The source code and model weights will be publicly available at https://github.com/losehu/QuaDreamer

See, Point, Fly: A Learning-Free VLM Framework for Universal Unmanned Aerial Navigation

Chih Yao Hu, Yang-Sen Lin, Yuna Lee, Chih-Hai Su, Jie-Ying Lee, Shr-Ruei Tsai, Chin-Yang Lin, Kuan-Wen Chen, Tsung-Wei Ke, Yu-Lun Liu

机构 * National Yang Ming Chiao Tung University（国家阳明交通大学） ； National Taiwan University（国立台湾大学） 纠错

Comments CoRL 2025. Project page: https://spf-web.pages.dev

Journal ref Proceedings of The 9th Conference on Robot Learning, PMLR 305:4697-4708, 2025

Cross-Sensor Touch Generation

Samanta Rodriguez, Yiming Dou, Miquel Oller, Andrew Owens, Nima Fazeli

机构 * University of Michigan（密歇根大学） ； Cornell University（康奈尔大学） 纠错

Comments CoRL 2025

BEVCALIB: LiDAR-Camera Calibration via Geometry-Guided Bird's-Eye View Representations

Weiduo Yuan, Jerry Li, Justin Yue, Divyank Shah, Konstantinos Karydis, Hang Qiu

机构 * University of Southern California（南加州大学） ； University of California, Riverside（加州大学河滨分校） 纠错

Journal ref 9th Conference on Robot Learning (CoRL 2025), Seoul, Korea

Robot Learning from Any Images

Siheng Zhao, Jiageng Mao, Wei Chow, Zeyu Shangguan, Tianheng Shi, Rong Xue, Yuxi Zheng, Yijia Weng, Yang You, Daniel Seita, Leonidas Guibas, Sergey Zakharov, Vitor Guizilini, Yue Wang

机构 * University of Southern California（南加州大学） ； Stanford University（斯坦福大学） ； Toyota Research Institute（丰田研究院） 纠错

Comments CoRL 2025 camera ready

mindmap: Spatial Memory in Deep Feature Maps for 3D Action Policies

Remo Steiner, Alexander Millane, David Tingdahl, Clemens Volk, Vikram Ramasamy, Xinjie Yao, Peter Du, Soha Pouya, Shiwei Sheng

机构 * NVIDIA（英伟达） ； Zurich Switzerland（苏黎世瑞士） ； Santa Clara California（圣克拉拉加州） 纠错

Comments Accepted to CoRL 2025 Workshop RemembeRL

Distilling On-device Language Models for Robot Planning with Minimal Human Intervention

Zachary Ravichandran, Ignacio Hounie, Fernando Cladera, Alejandro Ribeiro, George J. Pappas, Vijay Kumar

机构 * University of Pennsylvania（宾夕法尼亚大学） 纠错

Comments Accepted to the Conference on Robot Learning (CoRL) 2025

Motion Blender Gaussian Splatting for Dynamic Scene Reconstruction

Xinyu Zhang, Haonan Chang, Yuhan Liu, Abdeslam Boularias

机构 * Rutgers University（新泽西罗格斯大学） 纠错

Comments CoRL 2025

From Code to Action: Hierarchical Learning of Diffusion-VLM Policies

Markus Peschl, Pietro Mazzaglia, Daniel Dijkman

机构 * Qualcomm AI Research（高通人工智能研究） 纠错

Comments 19 pages including references, 6 figures. Accepted to CoRL LEAP 2025

FreeAction: Training-Free Techniques for Enhanced Fidelity of Trajectory-to-Video Generation

Seungwook Kim, Seunghyeon Lee, Minsu Cho

机构 * POSTECH ； Ewha Womans University（成均馆大学） ； RLWRLD 纠错

Comments 8 pages, 4 figures, accepted to CoRL 2025 LSRW workshop

LocoFormer: Generalist Locomotion via Long-context Adaptation

Min Liu, Deepak Pathak, Ananye Agarwal

机构 * Skild AI 纠错

Comments Accepted to CoRL 2025

Focusing on What Matters: Object-Agent-centric Tokenization for Vision Language Action models

Rokas Bendikas, Daniel Dijkman, Markus Peschl, Sanjay Haresh, Pietro Mazzaglia

机构 * Centre for Artificial Intelligence, UCL（人工智能中心，伦敦大学学院） ； Qualcomm AI Research（高通人工智能研究） 纠错

Comments Presented at 9th Conference on Robot Learning (CoRL 2025), Seoul, Korea

Mobi-$π$: Mobilizing Your Robot Learning Policy

Jingyun Yang, Isabella Huang, Brandon Vu, Max Bajracharya, Rika Antonova, Jeannette Bohg

机构 * Stanford University（斯坦福大学） ； Toyota Research Institute（丰田研究院） ； University of Cambridge（剑桥大学） 纠错

Comments CoRL 2025. Project website: https://mobipi.github.io/

Human-like Navigation in a World Built for Humans

Bhargav Chandaka, Gloria X. Wang, Haozhe Chen, Henry Che, Albert J. Zhai, Shenlong Wang

机构 * University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

Comments CoRL 2025. Project website: https://reasonnav.github.io/

Selective Progress-Aware Querying for Human-in-the-Loop Reinforcement Learning

Anujith Muraleedharan, Anamika J H

Comments Preprint. 8 pages, 3 figures, 1 table, 1 algorithm. CoRL 2025 style (preprint). Code/data to be released

Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories

Sunshine Jiang, Xiaolin Fang, Nicholas Roy, Tomás Lozano-Pérez, Leslie Pack Kaelbling, Siddharth Ancha

机构 * Massachusetts Institute of Technology（麻省理工学院） 纠错

Comments Conference on Robot Learning (CoRL) 2025