PCHands：面向N自由度机械臂的基于PCA的手部姿态协同表征
PCHands: PCA-based Hand Pose Synergy Representation on Manipulators with N-DoF
AI总结：
针对不同形态机械臂的灵巧操作通用表征学习问题，提出基于PCA的PCHands方法，通过锚点统一描述格式提取通用姿态协同主成分，在RL控制任务中表现更优且跨机械臂演示学习稳健。
摘要：
我们研究了为不同形态的机械臂学习灵巧操作通用表征的问题。为此，我们提出PCHands，一种从大量机械臂中提取手部姿态协同的新方法。我们基于锚点位置定义了简化统一的描述格式，适用于从二指夹爪到五指拟人手的各类机械臂。这使得我们能够学习机械臂构型的可变长度潜在表征，并对齐所有机械臂的末端执行器坐标系。我们证明，可以从该潜在表征中提取在不同结构和自由度的机械臂间通用的主成分。为评估PCHands，我们使用该紧凑表征对通过强化学习（RL）习得的灵巧操作任务控制策略的观测空间和动作空间进行编码。在学习效率和一致性方面，所提表征的表现优于在关节空间学习相同任务的基线方法。我们还证明，当演示来自不同机械臂时，PCHands在从演示中学习的RL场景中表现稳健。我们进一步通过涉及二指夹爪和四指拟人手的真实世界实验验证了结果。代码和补充材料可在https://hsp-iit.github.io/PCHands/获取。
英文摘要：
We consider the problem of learning a common representation for dexterous manipulation across manipulators of different morphologies. To this end, we propose PCHands, a novel approach for extracting hand postural synergies from a large set of manipulators. We define a simplified and unified description format based on anchor positions for manipulators ranging from 2-finger grippers to 5-finger anthropomorphic hands. This enables learning a variable-length latent representation of the manipulator configuration and the alignment of the end-effector frame of all manipulators. We show that it is possible to extract principal components from this latent representation that is universal across manipulators of different structures and degrees of freedom. To evaluate PCHands, we use this compact representation to encode observation and action spaces of control policies for dexterous manipulation tasks learned with RL. In terms of learning efficiency and consistency, the proposed representation outperforms a baseline that learns the same tasks in joint space. We additionally show that PCHands performs robustly in RL from demonstration, when demonstrations are provided from a different manipulator. We further support our results with real-world experiments that involve a 2-finger gripper and a 4-finger anthropomorphic hand. Code and additional material are available at https://hsp-iit.github.io/PCHands/.
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。
