[Submitted on 15 Sep 2026]
Title:Vision-Language Grounded Task-Context-Aware Imitation Learning for Robotic Disassembly
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Abstract: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 associating language-specified tasks with their corresponding manipulation targets in the visual scene. The proposed framework combines hierarchical task selection with task-context-aware imitation learning to ground language instructions in spatial visual representations for robotic disassembly. The resulting framework generalizes across diverse connector geometries and assembly configurations without requiring explicit object annotations. Our method improves end-to-end task success by 35 percentage points over the baseline diffusion policy and by 75 percentage points over the previous task-context-aware baseline.
Comments: Accepted for publication in IEEE Robotics and Automation Letters (RA-L), 2026. 8 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.17714 [cs.RO]
(or arXiv:2609.17714v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.17714
arXiv-issued DOI via DataCite (pending registration)
Related DOI:
https://doi.org/10.1109/LRA.2026.3734885
DOI(s) linking to related resources
Submission history
From: Jeon Ho Kang [view email] [v1] Tue, 15 Sep 2026 18:23:08 UTC (28,853 KB)
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