# Morphometric Imitation：从形态与接触感知手部重定向到零样本 sim-to-real 视觉运动策略

> 原标题：Morphometric Imitation: From Morphology and Contact Aware Hand Retargeting to Sim-to-Real Visuomotor Policy

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-23T00:00:00.000Z
- AIHOT：https://aihot.news/items/cmukmg13h1wx5ro9hcrfiv98d
- 原文：https://arxiv.org/abs/2609.28660

## 摘要

Morphometric Imitation 提出三阶段框架，将重建的人手-物体交互转化为零样本 sim-to-real 视觉运动策略。在三种机器人手和十种 HOI 上，其 MMO 的接触 F1 比五个基线中最强者至少高 8 分，下游动态重定向成功率最多提升 35 分；最终策略在 30 个物体、300 次真实世界试验中实现 89.3% 零样本成功率。

## 正文 · 原文

Human hand-object interactions (HOIs) provide a rich source of demonstrations for dexterous manipulation, but learning directly from them presents challenges in bridging morphology gaps, ensuring dynamical feasibility, and sim-to-real deployment. We present Morphometric Imitation, a three-stage framework that transforms reconstructed HOIs into zero-shot sim-to-real visuomotor policies. First, morphometric optimization (MMO) kinematically retargets human motion across hand morphologies while preserving demonstrated contacts. Second, residual reinforcement learning (RL) refines the kinematic reference using object pose and contact information from the human motion to produce dynamically feasible robot demonstrations. Third, these demonstrations are distilled into visuomotor policies. Across three robot hands and ten HOIs, MMO improves contact F1 over the strongest of five baselines by at least 8 points for every hand, while also improving the success rate of downstream dynamic retargeting by as much as 35 points. Ablations on the residual RL show complementary benefits from using object pose and contact information. Finally, the visuomotor policies achieve 89.3% zero-shot success in 300 real-world trials on 30 objects. Project page: [https://morphometricimitation.github.io{this](https://morphometricimitation.github.io{this/) https URL}