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

2026-09-15

AI Summary

为解决机器人拆卸任务中技能推断难题，本文通过结合语言任务上下文和视觉表征的方法提升模仿学习性能，提高任务成功率。

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.

Problem

• robotic disassembly
• long-horizon execution
• task context
• imitation learning
• language instructions

Innovation

• Vision-Language Grounded
• Task-Context-Aware Imitation Learning
• Hierarchical Task Selection
• Robotic Disassembly
• Spatial Visual Representations