面向灵巧双手操作的形态对称强化学习
Morphologically Symmetric Reinforcement Learning for Ambidextrous Bimanual Manipulation
AI总结：
本研究提出强化学习框架SYMDEX，以机器人双侧对称性为归纳偏置，通过等变神经网络复用双臂经验并蒸馏全局策略，提升双手及多臂操作的样本效率、鲁棒性与泛化能力。
摘要：
人类的粗大操作技能天然具有双侧对称性，能轻松在左右手之间镜像复刻简单动作。同样具备双侧对称性的双臂机器人也应利用这一特性，用任意一只手完成任务。人类通常会依赖惯用手完成精细灵巧技能，而机器人理想情况下应以同等熟练度实现双手灵巧操作。为此，我们提出SYMDEX（对称灵巧性），这是一种面向双手灵巧操作的强化学习框架，将机器人固有的双侧对称性作为归纳偏置加以利用。SYMDEX将复杂的双手操作任务分解为单只手的子任务，并为每只手训练专用策略。通过等变神经网络利用双侧对称性，一条手臂的经验天然能被对侧手臂复用。随后我们将这些子任务策略蒸馏为一个全局双手灵巧策略，该策略不受手-任务分配方式的影响。我们在6项具有挑战性的模拟操作任务上评估了SYMDEX，并在其中2项任务上完成了成功的真实世界部署。在左右手承担不同角色的复杂任务中，我们的方法性能大幅优于基线方法。我们进一步将SYMDEX扩展到四臂操作场景，验证了其可扩展性，其中具备对称感知的策略实现了高效的多臂协作与协调。我们的研究结果表明，将结构对称性作为策略学习中的归纳偏置，能够提升多样灵巧操作任务中的样本效率、鲁棒性和泛化能力。
英文摘要：
Humans naturally exhibit bilateral symmetry in their gross manipulation skills, effortlessly mirroring simple actions between left and right hands. Bimanual robots-which also feature bilateral symmetry-should similarly exploit this property to perform tasks with either hand. Unlike humans, who often favor a dominant hand for fine dexterous skills, robots should ideally execute ambidextrous manipulation with equal proficiency. To this end, we introduce SYMDEX (SYMmetric DEXterity), a reinforcement learning framework for ambidextrous bi-manipulation that leverages the robot's inherent bilateral symmetry as an inductive bias. SYMDEX decomposes complex bimanual manipulation tasks into per-hand subtasks and trains dedicated policies for each. By exploiting bilateral symmetry via equivariant neural networks, experience from one arm is inherently leveraged by the opposite arm. We then distill the subtask policies into a global ambidextrous policy that is independent of the hand-task assignment. We evaluate SYMDEX on six challenging simulated manipulation tasks and demonstrate successful real-world deployment on two of them. Our approach strongly outperforms baselines on complex task in which the left and right hands perform different roles. We further demonstrate SYMDEX's scalability by extending it to a four-arm manipulation setup, where our symmetry-aware policies enable effective multi-arm collaboration and coordination. Our results highlight how structural symmetry as inductive bias in policy learning enhances sample efficiency, robustness, and generalization across diverse dexterous manipulation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
