act-nutv4-ac40

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

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.