Adapt as You Say: Online Interactive Bimanual Skill Adaptation via Human Language Feedback
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866912985170051072 |
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| author | Li, Zhuo Li, Dianxi Teng, Tao Rouxel, Quentin Dong, Zhipeng Hong, Dennis Caldwell, Darwin Chen, Fei |
| author_facet | Li, Zhuo Li, Dianxi Teng, Tao Rouxel, Quentin Dong, Zhipeng Hong, Dennis Caldwell, Darwin Chen, Fei |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_26466 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Adapt as You Say: Online Interactive Bimanual Skill Adaptation via Human Language Feedback Li, Zhuo Li, Dianxi Teng, Tao Rouxel, Quentin Dong, Zhipeng Hong, Dennis Caldwell, Darwin Chen, Fei Robotics 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/. |
| title | Adapt as You Say: Online Interactive Bimanual Skill Adaptation via Human Language Feedback |
| topic | Robotics |
| url | https://arxiv.org/abs/2603.26466 |