---
title: "REGRIND：一种极简的重定向引导强化学习灵巧操作方案"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.11874v1"
permalink: "https://feed.astralor.com/items/itm_0a441b05c2deda5a4e3f68a2"
published: "2026-07-13T17:56:08.000Z"
score: 78
channel: "ai"
tags: ["REGRIND","仿真到真实迁移","强化学习","灵巧操作"]
---

# REGRIND：一种极简的重定向引导强化学习灵巧操作方案

## AI 点评

> 这篇论文把在人形全身控制上大放异彩的‘重定向+强化学习’recipe带到了灵巧操作里，而且只用一次人类演示就能训练出可迁移的策略，对实验室和工业落地都很有参考价值。如果你正被仿真到真实的‘最后一公里’折磨，这里面的系统辨识和域随机化分析会是实用干货。

## 速览

来自arXiv的最新论文提出REGRIND，一种仅需单次人类演示即可学习灵巧操作策略的极简流水线。该方法将人类手-物运动重定向至机器人参考轨迹，保留空间接触关系，通过仿真训练残差强化学习策略并零样本迁移至真实硬件。在剪刀和螺丝刀等接触丰富的工具使用任务中，多指灵巧手展现出流畅类人行为。系统消融实验揭示了影响仿真到真实迁移的关键因素。

## 关键点

- 提出极简流水线REGRIND，仅需单次人类演示即可学习灵巧操作策略。
- 通过重定向保留手-物空间与接触关系，结合残差强化学习与系统辨识实现零样本迁移。
- 在多指灵巧手上验证了剪刀操作和螺丝刀旋转等接触丰富任务，展现类人流畅行为。

## 全文

Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires delicate regulation of contact modes and forces. We present REGRIND, a minimalist retargeting-guided RL pipeline that learns dexterous manipulation policies from a single human demonstration. REGRIND retargets human hand-object motion to a robot reference that preserves hand-object spatial and contact relationships, trains a residual RL policy in simulation to track object-centric keypoints along that reference, and transfers the resulting policy zero-shot to hardware with careful system identification. The resulting policies produce fluid, human-like behavior on two different multi-fingered hands across contact-rich tool-use tasks, including operating a pair of scissors and turning a screwdriver. Through systematic hardware experiments, we identify and analyze the key factors that govern sim-to-real transfer in dexterous manipulation, offering practical guidance for retargeting-based learning in contact-rich settings. Videos and code are available at https://yunhaifeng.com/REGRIND.

---

引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_0a441b05c2deda5a4e3f68a2