Physical Time as a Missing Dimension of Robot Policy Memory Dynamics
Paper β’ 2610.20524 β’ Published β’ 3
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Check out the documentation for more information.
Checkpoints from a small-scale reproduction of Temporal Flow Policy (arXiv:2610.20524), Diffusion Policy backbone, Timed Button Press task (paper App. D.1 protocol). Full report: tourmii/tfp-repro (bucket README).
policy_tfp.pt, policy_gru.pt, policy_reactive.pt β 0.7 M-param state-only Diffusion Policies (128-width transformer denoiser, adaLN-zero, DDPM 100 cosine steps, x0-prediction, DDIM-25 at eval), 40k training steps with DP-style observation-noise augmentation. policy_tfp.pt includes the TFP memory module: 128-channel state, exponential relaxation h = e^{-Ξt/Ο}βh + (1-e^{-Ξt/Ο})βΔ₯ with candidate and per-channel time-constant heads (softplus, 1e-6 floor, zero-init Ο-head), AdaLN conditioning c = c_native + M(h).results.json β eval success rates (30 trials Γ 4 schedules Γ 3 variants), TFP frozen-weight interventions, event-age probe.metrics.jsonl β per-step training losses.demos_val.jsonl, demos_meta.json β validation episodes and protocol metadata (also in tourmii/tfp-button-demos-repro).arXiv:2610.20524 Β· Chi et al., Diffusion Policy, arXiv:2303.04137