PhasePlan: Ordered Future-Phase Planning for Robot Brain Models

2026-09-30

AI Summary

This study addresses the phase transition ambiguity and temporal misalignment caused by fixed-length action chunks in robotic action prediction. We propose an Ordered Future Phase Planning method that pioneers the use of phase prediction as an intermediate representation to guide action generation. Specifically, task phases at each action position are predicted from multimodal observations and subsequently condition the action generation process, achieving temporal alignment between task progress and action execution. A staged training strategy is adopted, wherein the planner is first trained and then frozen during action model adaptation to ensure stable phase representations. Evaluated on a conveyor belt manipulation task, our approach reduces offline joint action error by approximately 22.5% compared to baselines, significantly improving both phase transition modeling and cross-phase action prediction capabilities.

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 $π_{0.5}$ and AcrossWAM1.0 robot brain models. Detailed quantitative evaluation uses the $π_{0.5}$ implementation. On conveyor-belt manipulation, \method reduces offline joint-action error by approximately 22.5\% relative to the original $π_{0.5}$ 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.

Problem

• robot brain models
• action chunking
• phase transitions
• temporal alignment
• manipulation tasks

Innovation

• Robot Brain Models
• Future-Phase Planning
• Action Chunking
• Multimodal Observations
• Temporal Alignment