# PACT：用于多指模仿学习中手部构型与重定向的姿态对齐协同设计技术

> 原文链接：https://robotics.hellobike.com/papers/2026-10-13-2610-18443
> 频道：papers | 更新日期：2026-10-13 | 标签：arxiv, cs.RO
> 核心摘要：为提升遥操作示教姿态保真度，PACT联合优化机器手设计与重定向，并在遥操作和模仿学习中验证。

# PACT：用于多指模仿学习中手部构型与重定向的姿态对齐协同设计技术

::: v-pre
> 原文链接：[PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning](https://arxiv.org/abs/2610.18443)（arXiv 2610.18443 · 发布日期：2026-10-13）

**PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning**

为提升遥操作示教姿态保真度，PACT联合优化机器手设计与重定向，并在遥操作和模仿学习中验证。

## 核心要点

- 遥操作示教质量取决于机器手复现操作者姿态，传统设计常忽视类人抓握姿态。
- PACT基于人类抓握姿态，联合优化17舵机手机械参数与非线性重定向器。
- 优化对DexYCB人体姿态强制全指对应，并加入舵机干涉、装配间隙等约束。
- 实物PACT Hand在姿态误差、载荷、纸杯变形、YCB抓取和罐取放演示中优于基线。

**验证范围与局限**：验证包括实物PACT Hand的遥操作实验，以及罐取放任务中的一次模仿学习演示。

## 英文摘要 (Abstract)

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

作者：Koyo Sasazaki, Satoshi Funabashi, Pranav Ponnivalavan, Alexander Schmitz, Tetsuya Ogata, Shigeki Sugano
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## 原始参考来源 (Sources)

- [arXiv 2610.18443：PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning](https://arxiv.org/abs/2610.18443)