---
license: apache-2.0
base_model: nvidia/GR00T-N1.6-3B
tags:
- robotics
- gr00t
- unitree-g1
- inspire-hand
- manipulation
datasets:
- birbirll/g1-inspire-piston-pick-place
---

# GR00T N1.6 — G1 + Inspire piston pick-and-place

Fine-tune of `nvidia/GR00T-N1.6-3B` on
[birbirll/g1-inspire-piston-pick-place](https://huggingface.co/datasets/birbirll/g1-inspire-piston-pick-place)
(102 success episodes, LeRobot v2.1). Picks up a piston from a table with the
right Inspire hand (Unitree G1, fixed base). Closed-loop verified in IsaacLab.

## Contents

- Model weights (bf16 safetensors) + processor/experiment configs — loadable
  with Isaac-GR00T's `run_gr00t_server.py` (`--embodiment_tag NEW_EMBODIMENT`).
- **`g1_inspire_modality_config.py`** — the modality config used for training
  (register via `--modality-config-path`): state = arms 14 + hands 12 + waist 3
  (dims 29:63 of the raw state are unused tactile); action = 30-D
  (left_arm 7 | right_arm 7 | left_hand 6 | right_hand 6 | base_height 1 |
  navigate 3), 30-step horizon; arms trained RELATIVE (decoded to absolute by
  the server).

## Training recipe (single RTX 4090, ~2.5 h)

```bash
python gr00t/experiment/launch_finetune.py \
  --base-model-path nvidia/GR00T-N1.6-3B \
  --dataset-path <local dataset> \
  --embodiment-tag NEW_EMBODIMENT \
  --modality-config-path g1_inspire_modality_config.py \
  --num-gpus 1 --output-dir ./out \
  --max-steps 10000 --save-steps 1000 --save-total-limit 2 \
  --global-batch-size 8 --gradient-accumulation-steps 4 \
  --state-dropout-prob 0.8 \
  --color-jitter-params brightness 0.3 contrast 0.4 saturation 0.5 hue 0.08
```

Key knobs: `--state-dropout-prob 0.8` (forces vision conditioning — without it
the policy shortcuts through proprioception and ignores the camera);
effective batch 32; default LR 1e-4; loss ~1.16 → ~0.011. On 24 GB GPUs you
must additionally set the optimizer to `paged_adamw_8bit` and enable gradient
checkpointing in `launch_finetune.py` (upstream defaults OOM); on A100-class
hardware the upstream defaults are fine.