根据指令适应：通过人类语言反馈实现在线交互双臂技能适应
Adapt as You Say: Online Interactive Bimanual Skill Adaptation via Human Language Feedback
AI总结：
本文提出BiSAIL框架，通过交互语言反馈实现零样本在线适应，提升双臂技能在任务变化中的适应性、泛化能力和跨身体可扩展性。
摘要：
开发能够自主在人类居住环境中运作的通用机器人需要能够适应持续变化的任务条件。然而，部署时将高维协调双臂技能适应到新任务变化仍是一个基本挑战。本文提出BiSAIL（通过交互语言实现双臂技能适应）框架，一种新的方法，通过交互语言反馈实现零样本在线适应。BiSAIL的核心思想是采用分层推理-然后调节范式，首先从多模态任务变化推断出通用的适应目标，然后通过扩散调节适应双臂动作以实现推断的目标。广泛的实际机器人实验在六个双臂任务和两个双臂平台中显示，BiSAIL在人机交互适应性、任务泛化和跨身体可扩展性方面显著优于现有方法。这项工作使开发能够通过直观的口头修正灵活定制的适应双臂助手成为可能。实验视频和代码可在https://rip4kobe.github.io/BiSAIL/上获得。
英文摘要：
Developing general-purpose robots capable of autonomously operating in human living environments requires the ability to adapt to continuously evolving task conditions. However, adapting high-dimensional coordinated bimanual skills to novel task variations at deployment remains a fundamental challenge. In this work, we present BiSAIL (Bimanual Skill Adaptation via Interactive Language), a novel framework that enables zero-shot online adaptation of offline-learned bimanual skills through interactive language feedback. The key idea of BiSAIL is to adopt a hierarchical reason-then-modulate paradigm, which first infers generalized adaptation objectives from multimodal task variations, and then adapts bimanual motions via diffusion modulation to achieve the inferred objectives. Extensive real-robot experiments across six bimanual tasks and two dual-arm platforms demonstrate that BiSAIL significantly outperforms existing methods in human-in-the-loop adaptability, task generalization and cross-embodiment scalability. This work enables the development of adaptive bimanual assistants that can be flexibly customized by non-expert users via intuitive verbal corrections. Experimental videos and code are available at https://rip4kobe.github.io/BiSAIL/.
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。
