ACT — nutv4, chunk 40
- Dataset: Kaz55/dg5f_ur5e_nutv4 — 60 episodes / 83,311 frames
- GelSight: 500x375 (native) x2
- RealSense: 640x480 x2
- Policy: ACT, chunk_size=40, n_action_steps=40
- Training: 100,000 steps (~9.6 epochs), batch 8, seed 1000
Inputs
observation.state (26) + RealSense x2 + GelSight x2
observation.velocity and observation.effort exist in the dataset but are
deliberately excluded — feature auto-derivation would otherwise feed them to
the policy and add a second difference between runs.
Chunk-length pair
| chunk | model | final train loss |
|---|---|---|
| 40 | act-nutv4-ac40 | 0.085 |
| 60 | act-nutv4-ac60 | 0.085 |
Both converged to the same training loss. Note that loss is normally not comparable across chunk lengths (they predict a different number of future actions), and on the related blue-cable sweeps training loss stayed flat even when GelSight was removed entirely. Pick a chunk length by on-robot evaluation, not by these numbers.
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