PACT：用于多指模仿学习中手部配置与重定向的姿态协同设计技术
PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning
AI总结：
PACT是一种联合优化机器人手机械参数与姿态重定向器的技术，其制造的PACT手在姿态复现精度、负载能力、抓握成功率等指标上优于LEAP Hand基线，基于其演示训练的模仿学习策略在罐头抓取任务中表现良好。
摘要：
用于模仿学习的遥操作演示的保真度取决于机器人手能否忠实地复现操作者的姿态。机器人将执行的姿态重定向律通常仅在硬件确定后才被选定，而传统手部设计常忽略类人抓握姿态。我们提出PACT，一种基于人类抓握姿态的优化方法，该方法联合优化17个伺服电机驱动的机器人手的机械参数（连杆长度、关节偏移量、安装角及基座位置）及其非线性姿态重定向器（线性人-机器人关节映射、各关节姿态逆运动学权重及阻尼）。该优化方法确保全手指（各手指的MCP、PIP、DIP及指尖）与DexYCB数据集中的人类姿态对应，并受伺服电机干涉、装配间隙及平手突出度约束，使所得设计可物理装配。我们制造了PACT手并通过遥操作实验和模仿学习演示验证：该手以3.9mm的运动学均方根误差复现所有人类手部关键点，而LEAP Hand基线的误差为12.8mm；在所有抓握姿态下，其指尖均保持在10mm以内，而基线仅21.6%的姿态满足该要求。在匹配的各关节扭矩极限下，其负载能力为基线的两倍（2kg vs 1kg），且对所持纸杯的变形量减少7倍，达到与人类手相当的变形水平。其在YCB日常物体上的抓握成功率为86%，而基线为64%。最后，作为演示，用PACT手收集的演示训练的模仿学习策略在罐头抓取-放置任务的未见过放置位置上达到75%的成功率。项目页面：this https URL
英文摘要：
The fidelity of teleoperated demonstrations used for imitation learning is determined by how faithfully a robot hand can replicate the operator's posture. The posture retargeting law that the robot will execute is typically selected only once the hardware has been finalized, and conventional hand designs often neglect human-like grasping postures. We introduce PACT, an optimization based on human grasping postures that jointly optimizes the mechanical parameters of a 17-servo robot hand (link lengths, joint offsets, mounting angles, and base placements) and its nonlinear posture retargeter (a linear human-to-robot joint map, per-joint posture-IK weights, and damping). The optimization enforces full-finger correspondence (MCP, PIP, DIP, and tip of each finger) to human poses from the DexYCB dataset, and is constrained by servo interference, assembly clearances, and flat-hand protrusion, so the resulting design can be physically assembled. We fabricate the PACT Hand and validate it through teleoperation experiments and an imitation-learning demonstration. The hand reproduces all human hand keypoints with a kinematic RMSE of 3.9 mm, compared to 12.8 mm for the LEAP Hand baseline, and keeps all fingertips within 10 mm on every grasping posture, while the baseline achieves this for only 21.6% of the poses. Under matched per-joint torque limits, it lifts twice the baseline payload, 2 kg against 1 kg, and deforms a held paper cup seven times less, reaching a deformation level comparable to a human hand. It also achieves an 86% grasping success rate on YCB daily objects, compared to 64% for the baseline. Finally, as a demonstration, an imitation-learning policy trained on demonstrations collected with the PACT Hand achieved a 75% success rate at held-out placements in a can pick-and-place task. Project page: https://pact-hand-2027.github.io
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。
