物理时间作为机器人策略记忆动力学的缺失维度
Physical Time as a Missing Dimension of Robot Policy Memory Dynamics
AI总结：
研究发现机器人策略记忆缺失物理时间维度，提出TFP策略学习记忆动力学，经实验验证其在计时、记忆任务及真实机器人操作中表现优于现有方法。
摘要：
现有机器人策略记忆通常随观测序列演化，隐含地将每次更新视为相同的物理时间量。然而，相同的观测历史可跨越不同时长并要求不同动作，这表明物理时间是策略记忆的缺失维度。我们引入时间流策略（Temporal Flow Policy, TFP），该策略学习记忆动力学以保留过去事件并在测量的时间间隔上演化记忆状态。观测值和先验记忆决定每次转移的内容和时间尺度；经过的时间决定状态推进的程度。TFP通过该记忆状态对扩散策略和流匹配策略进行条件化，并使用它们的原生模仿目标进行训练。我们通过对记忆设计的受控比较，结合扩散策略和π₀.₅评估TFP。这些实验表明，成功的计时既取决于测量的间隔，也取决于保留的历史。在我们配备相同间隔的循环记忆和状态空间记忆，以及将间隔输入学习门的变体相比，TFP实现了更高的平均成功率。记忆读出可恢复自事件发生以来的时间以及遮挡后的运动信息。改变提供的间隔会系统性地延迟或提前动作，将时间输入与行为关联起来。需要计时或记忆的真实机器人任务，以及LIBERO评估，证明了其在物理和通用操作中的优势。这些结果表明，应将机器人记忆视为并非存储的观测历史，而是保留过去事件并随物理时间演化的状态。
英文摘要：
Existing robot policy memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. Yet identical observation histories can span different durations and require different actions, exposing physical time as a missing dimension of policy memory. We introduce Temporal Flow Policy (TFP), which learns memory dynamics to retain past events and evolve the memory state over measured time intervals. Observations and prior memory determine the content and time scales of each transition; elapsed time determines how far the state advances. TFP conditions diffusion and flow-matching policies through this memory state and is trained with their native imitation objectives. We evaluate TFP with Diffusion Policy and $π_{0.5}$ through controlled comparisons of memory designs. These experiments show that successful timing depends on both measured intervals and retained history. TFP achieves higher average success than the recurrent and state-space memories we equip with the same intervals and than a variant that feeds the interval to a learned gate. Memory readouts recover the time since an event and motion information after occlusion. Changing the supplied intervals systematically delays or advances actions, linking time inputs to behavior. Real-robot tasks requiring timing or memory, together with LIBERO evaluations, demonstrate benefits in physical and general manipulation. These results suggest viewing robot memory not as stored observation history, but as a state that retains past events and evolves with physical time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
