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/.

翻译：开发能够在人类生活环境中自主运行的通用型机器人，需要其具备适应不断变化任务条件的能力。然而，在部署过程中将高维协调的双臂技能适配至新型任务变体，仍是一项根本性挑战。本文提出BiSAIL（基于交互式语言的双臂技能适应框架），一种通过交互式语言反馈实现离线学习双臂技能零样本在线适应的新颖框架。BiSAIL的核心思想采用层次化"推理-调制"范式：首先从多模态任务变体中推断广义适应目标，随后通过扩散调制适配双臂运动以实现推断目标。在六项双臂任务及两个双机械臂平台上开展的大量实物机器人实验表明，BiSAIL在人机环适应能力、任务泛化性及跨本体可扩展性方面显著优于现有方法。本研究推动了自适应双臂助手的开发，使非专业用户能够通过直观的口头修正灵活定制机器人行为。实验视频与代码详见https://rip4kobe.github.io/BiSAIL/。