Bimanual dexterous manipulation for tool use remains a formidable challenge in robotics due to the high-dimensional state space and complicated contact dynamics. Existing methods naively represent the entire system state as a single configuration vector, disregarding the rich structural and topological information inherent to articulated hands. We present PhysGraph, a physically-grounded graph transformer policy designed explicitly for challenging bimanual hand-tool-object manipulation. Unlike prior works, we represent the bimanual system as a kinematic graph and introduce per-link tokenization to preserve fine-grained local state information. We propose a physically-grounded bias generator that injects structural priors directly into the attention mechanism, including kinematic spatial distance, dynamic contact states, geometric proximity, and anatomical properties. This allows the policy to explicitly reason about physical interactions rather than learning them implicitly from sparse rewards. Extensive experiments show that PhysGraph significantly outperforms baseline - ManipTrans in manipulation precision and task success rates while using only 51% of the parameters of ManipTrans. Furthermore, the inherent topological flexibility of our architecture shows qualitative zero-shot transfer to unseen tool/object geometries, and is sufficiently general to be trained on three robotic hands (Shadow, Allegro, Inspire).
UniBiDex: A Unified Teleoperation Framework for Robotic Bimanual Dexterous Manipulation
Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, The Great Bay University（东莞智能装备与智能制造重点实验室，高级工程学院，大湾区大学）；Department of Mechanical Engineering, The Hong Kong Polytechnic University（香港理工大学机械工程系）；Guangdong Academy of Sciences（广东省科学院）
DexRoam: Learning Mobile Bimanual Dexterous Manipulation from Egocentric Whole-Body Human Demonstrations
The Hong Kong University of Science and Technology（香港科技大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University（北京大学计算机学院多媒体信息处理国家重点实验室）；Beihang University（北京航空航天大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
HumanoidGen: Data Generation for Bimanual Dexterous Manipulation via LLM Reasoning
Fudan University（复旦大学）；Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；University of Science and Technology of China（中国科学技术大学）；East China University of Science and Technology（东华大学）
YUBI: Yielding Universal Bidigital Interface for Bimanual Dexterous Manipulation at Scale
AI Robot Association (AIRoA)（AI机器人协会）；Toyota Motor Corporation（丰田汽车公司）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；The University of Tokyo（东京大学）；Institute of Science Tokyo（东京科学研究所）；Waseda University（早稻田大学）
DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter
Beijing Academy of Artificial Intelligence, Beijing, China（北京人工智能研究院）；School of Computation, Information and Technology, Technical University of Munich, Garching, Germany（慕尼黑技术大学计算与信息学院）；Department of Shenyang Institute of Computing Technology, University of Chinese Academy of Sciences, Beijing, China（中国科学院沈阳计算技术研究所部门）；State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China（新型软件技术国家重点实验室）；Department of Computer Science and Technology, Tsinghua University, Beijing, China（清华大学计算机科学与技术系）
Institute for AI, Peking University（北京大学人工智能研究院）；Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；University College London（伦敦大学学院）；Carnegie Mellon University（卡内基梅隆大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）
Achieving human-level dexterity is an important open problem in robotics. However, tasks of dexterous hand manipulation, even at the baby level, are challenging to solve through reinforcement learning (RL). The difficulty lies in the high degrees of freedom and the required cooperation among heterogeneous agents (e.g., joints of fingers). In this study, we propose the Bimanual Dexterous Hands Benchmark (Bi-DexHands), a simulator that involves two dexterous hands with tens of bimanual manipulation tasks and thousands of target objects. Specifically, tasks in Bi-DexHands are designed to match di…
ManiDP: Manipulability-Aware Diffusion Policy for Posture-Dependent Bimanual Manipulation
Department of Mechanical and Automation Engineering, T-Stone Robotics Institute, The Chinese University of Hong Kong（机械与自动化工程系、T-Stone机器人研究所、香港中文大学）；Hong Kong Center for Logistics Robotics（香港物流机器人中心）；Department of Technological Sciences, Wuhan University（技术科学系、武汉大学）；Idiap Research Institute（Idiap研究所）；Department of Advanced Robotics, Istituto Italiano di Tecnologia（先进机器人系、意大利Istituto Italiano di Tecnologia）
DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo
NLPR & MAIS, CASIA（中国科学院南京自动化研究所与模式识别与人工智能实验室）；SJTU（上海交通大学）；MBZUAI（马克斯·普朗克研究所）；Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；PKU & Galbot（北京大学与Galbot）；CUHK（香港大学）
Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
TU Darmstadt（达姆施塔特工业大学）；Honda Research Institute Europe（本田欧洲研究所）；Istituto Italiano di Tecnologia（意大利理工学院）；DFKI（德国人工智能研究中心）；Robotics Institute Germany（德国机器人研究所）
Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University（机械系统与振动国家重点实验室，机械工程学院，上海交通大学）；Shanghai Key Laboratory of Intelligent Robotics, Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China（智能机器人上海市重点实验室，元机器人研究院，上海交通大学，上海200240，中国）；The University of Tokyo（东京大学）
BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes
School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；School of Computer Science, Peking University（北京大学计算机学院）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
Shanghai Jiao Tong University（上海交通大学）；Sharpa；Shanghai AI Lab（上海人工智能实验室）；Zhongguancun Academy（中关村学院）；National University of Singapore（新加坡国立大学）；Shanghai Innovation Institute（上海创新研究院）
UltraDexGrasp: Learning Universal Dexterous Grasping for Bimanual Robots with Synthetic Data
Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Zhejiang University（浙江大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）
Learning dexterous manipulation in high-dimensional state-action spaces is an important open challenge with exploration presenting a major bottleneck. Although in many cases the learning process could be guided by demonstrations or other suboptimal experts, current RL algorithms for continuous action spaces often fail to effectively utilize combinations of highly off-policy expert data and on-policy exploration data. As a solution, we introduce Relative Entropy Q-Learning (REQ), a simple policy iteration algorithm that combines ideas from successful offline and conventional RL algorithms. It r…
