Training robotic policies in simulation suffers from the sim-to-real gap, as simulated dynamics can be different from real-world dynamics. Past works tackled this problem through domain randomization and online system-identification. The former is sensitive to the manually-specified training distribution of dynamics parameters and can result in behaviors that are overly conservative. The latter requires learning policies that concurrently perform the task and generate useful trajectories for system identification. In this work, we propose and analyze a framework for learning exploration policies that explicitly perform task-oriented exploration actions to identify task-relevant system parameters. These parameters are then used by model-based trajectory optimization algorithms to perform the task in the real world. We instantiate the framework in simulation with the Linear Quadratic Regulator as well as in the real world with pouring and object dragging tasks. Experiments show that task-oriented exploration helps model-based policies adapt to systems with initially unknown parameters, and it leads to better task performance than task-agnostic exploration.
A Study on Enhancing the Generalization Ability of Visuomotor Policies via Data Augmentation
University of Chinese Academy of Sciences（中国科学院大学）；School of Artificial Intelligence & Institute of Automation, Chinese Academy of Sciences & NLPR（中国科学院人工智能学院与自动化研究所）
Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies
Department of Automation, Tsinghua University（清华大学自动化系）；School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University（北京交通大学机械与电子控制工程学院）；Booster Robotics Technology Co., Ltd（北京加速进化科技有限公司）
Learning Structured Robot Policies from Vision-Language Models via Synthetic Neuro-Symbolic Supervision
University of Padova, Dept. of Information Engineering（帕多瓦大学信息工程系）；Fraunhofer Italia Research（弗劳恩霍夫意大利研究所）；Polytechnic of Bari Dept. of Electrical and Information Engineering（巴里理工学院电气与信息工程系）
Bridging the Gap between Discrete Agent Strategies in Game Theory and Continuous Motion Planning in Dynamic Environments
Department of Electrical and Systems Engineering, University of Pennsylvania（电气与系统工程系，宾夕法尼亚大学）；Department of Mathematics, University of Pennsylvania（数学系，宾夕法尼亚大学）
Towards Bridging the Gap between Large-Scale Pretraining and Efficient Finetuning for Humanoid Control
State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；School of Artificial Intelligence, Xidian University（人工智能学院，西安电子科技大学）
PLaW-VLA: Predictive Latent World Modeling for Vision-Language-Action Policies
Jilin University（吉林大学）；Astribot；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Hong Kong Polytechnic University（香港理工大学）；The University of Tokyo（东京大学）
Posterior Optimization with Clipped Objective for Bridging Efficiency and Stability in Generative Policy Learning
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CFCS, School of Computer Science, Peking University（北京大学计算机学院前沿计算研究中心）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）
Dexterous Control of an 11-DOF Redundant Robot for CT-Guided Needle Insertion With Task-Oriented Weighted Policies
Jacobs School of Engineering, University of California San Diego（加州大学圣地亚哥分校 Jacobs 工程学院）；School of Medicine, University of California San Diego（加州大学圣地亚哥分校医学院）；School of Medicine, University of Missouri-Kansas City（密苏里大学堪萨斯城分校医学院）
DexCanvas: Bridging Human Demonstrations and Robot Learning for Dexterous Manipulation
DexRobot Co. Ltd.（德克斯机器人有限公司）；University of Michigan（密歇根大学）；Shanghai Jiao Tong University（上海交通大学）；Chongqing University（重庆大学）；East China University of Science and Technology（东华大学）
