[2603.05160] Lifelong Language-Conditioned Robotic Manipulation Learning

[2603.05160] Lifelong Language-Conditioned Robotic Manipulation Learning

arXiv - AI 3 min read

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Abstract page for arXiv paper 2603.05160: Lifelong Language-Conditioned Robotic Manipulation Learning

Computer Science > Robotics arXiv:2603.05160 (cs) [Submitted on 5 Mar 2026] Title:Lifelong Language-Conditioned Robotic Manipulation Learning Authors:Xudong Wang, Zebin Han, Zhiyu Liu, Gan Li, Jiahua Dong, Baichen Liu, Lianqing Liu, Zhi Han View a PDF of the paper titled Lifelong Language-Conditioned Robotic Manipulation Learning, by Xudong Wang and 7 other authors View PDF HTML (experimental) Abstract:Traditional language-conditioned manipulation agent sequential adaptation to new manipulation skills leads to catastrophic forgetting of old skills, limiting dynamic scene practical deployment. In this paper, we propose SkillsCrafter, a novel robotic manipulation framework designed to continually learn multiple skills while reducing catastrophic forgetting of old skills. Specifically, we propose a Manipulation Skills Adaptation to retain the old skills knowledge while inheriting the shared knowledge between new and old skills to facilitate learning of new skills. Meanwhile, we perform the singular value decomposition on the diverse skill instructions to obtain common skill semantic subspace projection matrices, thereby recording the essential semantic space of skills. To achieve forget-less and generalization manipulation, we propose a Skills Specialization Aggregation to compute inter-skills similarity in skill semantic subspaces, achieving aggregation of the previously learned skill knowledge for any new or unknown skill. Extensive experiments demonstrate the effectiveness...

Originally published on March 06, 2026. Curated by AI News.

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