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Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly

arXiv · AI, language, vision and robotics · article · Sep 15, 2026 · UTC

Real-world robotic disassembly requires long-horizon execution, where robots must perform ordered sequences of manipulation tasks across multiple parts within a single scene. Multiple valid task goals and diverse assembly configurations make it difficult for imitation policies to infer the intended skill from raw observations alone, particularly when training data cannot cover the combinatorial diversity of real-world configurations and part geometries. We show that incorporating task context through language alleviates these challenges by providing explicit structure for skill selection and a

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