Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
For robotic manipulation researchers, this work addresses the bottleneck of composable skill knowledge extraction, enabling zero-shot generalization to unseen tasks without parameter updates.
The paper tackles cross-task generalization in robotic manipulation by introducing a skill reasoning framework that decomposes seen demonstrations into atomic skill-action pairs and recomposes them for unseen tasks. The method achieves zero-shot cross-task generalization, validated on the AGNOSTOS benchmark and real-world environments.
Cross-task generalization is a core challenge in open-world robotic manipulation, and the key lies in extracting transferable manipulation knowledge from seen tasks. Recent in-context learning approaches leverage seen task demonstrations to generate actions for unseen tasks without parameter updates. However, existing methods provide only low-level continuous action sequences as context, failing to capture composable skill knowledge and causing models to degenerate into superficial trajectory imitation. We propose Decompose and Recompose, a skill reasoning framework using atomic skill-action pairs as intermediate representations. Our approach decomposes seen demonstrations into interpretable skill--action alignments, enabling the model to recompose these skills for unseen tasks through compositional reasoning. Specifically, we construct a task-adaptive dynamic demonstration library via visual-semantic retrieval combined with skill sequences from a planning agent, complemented by a coverage-aware static library to fill missing skill patterns. Together, these yield skill-comprehensive demonstrations that explicitly elicit compositional reasoning for skill composition and execution ordering. Experiments on the AGNOSTOS benchmark and real-world environments validate our method's zero-shot cross-task generalization capability.