Existing robot policy memories typically evolve with observation sequences, implicitly treating each update as the same amount of physical time. This paper introduces Temporal Flow Policy (TFP), which learns memory dynamics to retain past events and evolve the memory state over measured time intervals. Experiments show that successful timing depends on both measured intervals and retained history.
The chart will appear after repeat observations. The current metric comes from the source.
2 source votesReal observations only. History before source connection is not reconstructed.
TFP can improve the accuracy of temporal actions in robots by considering physical time in memory.
The study presents AgentSWE, a benchmark evaluating coding agents' ability to build agents aligned with user requirements. The research found that existing agents fail to accurately fulfill tasks, as there is a significant gap between demonstrated and actual performance.
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