PhasePlan: Ordered Future-Phase Planning for Robot Brain Models

arXiv:2609.39970v1 Announce Type: new Abstract: Robot brain models integrate vision, language, and robot state to generate actions for complex manipulation tasks. Most predict fixed-length action chunks that may span multiple task phases. This can obscure phase transitions and favor frequent action patterns, compromising action timing in dynamic environments. We propose method, an ordered future-phase planning method for robot brain models. From current multimodal observations, it predicts the task phase at each future action position. The resulting planning representations condition the corresponding actions, preserving temporal alignment between task progress and action generation. Training first learns the planner, then freezes it during action-model adaptation to maintain stable phase representations. We instantiate method on pretrained pi_{0.5} and AcrossWAM1.0 robot brain models. Detailed quantitative evaluation uses the pi_{0.5} implementation. On conveyor-belt manipulation, method reduces offline joint-action error by approximately 22.5% relative to the original pi_{0.5} model. It also improves phase-transition modeling and cross-phase action prediction. These results demonstrate the value of ordered future-phase planning for continuous action generation.

Source: arXiv cs.RO | 2026-10-01