Task-Context-Aware Diffusion Policy with Language Guidance for Multi-task Disassembly

Diffusion-based policy learning has shown strong performance across diverse robotic tasks, often achieving high success rates. However, real-world deployment requires more than task success—it demands efficient execution and the ability to handle complex environments. In many assembly and disassembly settings, a single scene contains multiple potential task goals. This can confuse learned policies, leading to ambiguous behavior. Enabling explicit task selection via natural language is thus crucial for robust and flexible operation. In this paper, we address two key challenges: (1) improving task execution efficiency by structuring tasks into distinct sub-task modes using language, and (2) resolving goal ambiguity by allowing human operators to specify desired tasks through natural language commands. We further introduce an adaptive parameter selection mechanism that adjusts reliance on different sensory modalities depending on the active sub-task. We evaluate our approach on the NIST Task Board, a representative benchmark with multiple co-located task goals. Our method improves execution speed by 57% and increases task success rate by 19% compared to baseline approaches. Demonstration videos are available at: https://rros-lab.github.io/task-aware-diffusion/.