PhasePlan：面向机器人脑模型的有序未来阶段规划
PhasePlan: Ordered Future-Phase Planning for Robot Brain Models
AI总结：
PhasePlan通过预测未来动作位置的任务阶段，为机器人脑模型提供有序未来阶段规划，改善阶段转换和动作时序，在传送带操作中降低约22.5%的联合动作误差。
AI中文摘要：
机器人脑模型整合视觉、语言和机器人状态信息，以生成复杂操作任务的行动。大多数模型预测固定长度的动作块，这些动作块可能跨越多个任务阶段。这可能会模糊阶段转换，并偏向频繁出现的动作模式，从而在动态环境中损害动作的时序性。我们提出PhasePlan，一种用于机器人脑模型的有序未来阶段规划方法。该方法从当前多模态观测中预测每个未来动作位置的任务阶段。由此产生的规划表示对相应动作进行条件化，保持了任务进展与动作生成之间的时间对齐。训练过程首先学习规划器，然后在动作模型适应期间将其冻结，以维持稳定的阶段表示。我们在预训练的π₀.₅和AcrossWAM1.0机器人脑模型上实例化PhasePlan。详细的定量评估使用了π₀.₅实现。在传送带操作任务中，PhasePlan相对于原始π₀.₅模型将离线联合动作误差降低了约22.5%。它还改善了阶段转换建模和跨阶段动作预测。这些结果证明了有序未来阶段规划对连续动作生成的价值。
英文摘要：
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.
