---
library_name: lerobot
pipeline_tag: robotics
tags: [act, lerobot, robotics, dg5f, ur5e, gelsight]
---

# ACT — nutv4, chunk 40

- **Dataset**: [Kaz55/dg5f_ur5e_nutv4](https://huggingface.co/datasets/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.