OmniTacTune: 策略无关的真实世界强化学习用于视觉策略的触觉残差适应

摘要

从人类视频、遥操作和机器人演示中学习到的视觉策略提供了可扩展的运动先验，但在接触密集型操作中往往失效，因为此类任务的成功高度依赖于局部力和接触几何。触觉传感提供了这些互补信号，然而触觉数据的采集成本高昂，且难以在不同传感器、机器人和任务间泛化。我们提出OmniTacTune——一种与策略无关的真实世界强化学习流水线，通过残差校正将触觉反馈适配到预训练的视觉策略中。OmniTacTune采用两阶段设计：首先从基策略的自主推演中引导出触觉感知学习，随后通过在线交互学习一个轻量级的触觉残差策略。大量实验表明，OmniTacTune在多种接触密集型任务、视觉基策略和触觉表示中均表现出泛化能力。在四项真实世界接触密集型任务中，它能在40-80分钟内将视觉基策略的成功率从5%-40%提升至85%-100%，展示了将触觉反馈高效融入可扩展视觉机器人策略的路径。项目页面：https://colinyu1.github.io/omnitactune-site/

English

Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipulation, where success significantly depends on local force and contact geometry. Tactile sensing provides these complementary signals, yet tactile data remain costly to collect and hard to generalize across sensors, robots, and tasks. We introduce OmniTacTune, a policy-agnostic real-world RL pipeline that adapts tactile feedback to pretrained visual policies through residual correction. OmniTacTune uses a two-stage design: it first bootstraps tactile-aware learning from autonomous base-policy rollouts, then learns a lightweight tactile residual policy through online interaction. Extensive experiments show that OmniTacTune generalizes across diverse contact-rich tasks, visual base policies, and tactile representations. Across four real-world contact-rich tasks, it improves visual base policies from 5-40% success to 85-100% within 40-80 minutes, demonstrating an efficient path for adapting tactile feedback to scalable visual robot policies. Project page: https://colinyu1.github.io/omnitactune-site/