---
license: other
license_name: nvidia-open-model-license
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
base_model: nvidia/GR00T-N1.7-3B
tags:
  - robotics
  - humanoid
  - vla
  - gr00t
  - asimov
  - mujoco
pipeline_tag: robotics
---

# GR00T N1.7 for the Asimov 1 humanoid (simulation)

A fine-tune of [NVIDIA GR00T N1.7-3B](https://huggingface.co/nvidia/GR00T-N1.7-3B) for
[Menlo Research's Asimov 1](https://github.com/menloresearch/asimov-1), an open-source humanoid, in the
configuration that actually ships: 25 DOF, passive printed "dummy hands" (no grippers), one monocular head
camera. As far as we know this is the first GR00T policy for Asimov.

**Simulation only.** It has not been run on a real robot.

## Tasks and results

Three language-conditioned tabletop tasks in MuJoCo. Evaluation is closed loop on 50 held-out layouts per
task, with seeds disjoint from every training seed. The policy runs at 20 Hz, predicts 16-step chunks and
executes 8 of them per call. Success must hold for 0.5 s.

| Task | Instruction example | ckpt 6000 | **ckpt 10000** | 95% CI (10000) |
|---|---|---|---|---|
| Push (one fist) | "push the purple block onto the orange pad" | 46% | **84%** | 0.71–0.92 |
| Button | "press the red button" | 36% | **64%** | 0.50–0.76 |
| Box (bimanual squeeze, carry, place) | "lift the box and put it on the shelf" | 2% | **56%** | 0.42–0.69 |

- **Main failures at step 10000:**
  - push: block short of the pad (5), block never moved (3)
  - button: no press (10), partial press (6), wrong button (2)
  - box: dropped or not placed (16)
- **Not converged:** success was still rising steeply between steps 6000 and 10000, so longer training should help.
- **Open-loop** on training episodes: action MAE 0.027 rad.
- **Inference:** 95 ms per call on an L40 (PyTorch eager).

Example rollouts (one success and one failure per task) are in `results/closedloop_checkpoint-10000/videos/`.

## Training

- **Data:** 900 scripted-expert demonstrations, 300 per task. That is 155k frames at 20 Hz, 43 instructions,
  randomized object poses, colors, distractors, lighting and camera jitter.
- **Observation:**
  - head camera, 256×256
  - joint positions: left and right arm (5 each), waist yaw, neck yaw and pitch
  - the instruction
- **Action:** absolute joint targets for the same 13 DOF, learned as RELATIVE (delta from current state),
  16-step horizon.
- **Fine-tune:** `NEW_EMBODIMENT`, default GR00T recipe (projector and action head trained, backbone frozen).
  10,000 steps at batch 64, learning rate 1e-4, color jitter. One L40 for 2 h 43 m. Loss went from 0.095 to 0.010.
- **Checkpoints:** `checkpoints/checkpoint-{2000..10000}`, with weights and processor but no optimizer state.

## Simplifications and assumptions

These are deliberate scope cuts for v1:

- **Pelvis welded:** legs held at the standing pose. Balance is the locomotion controller's job on hardware.
- **Hand collision shapes:** convex hulls of Menlo's printed dummy-hand files. Mass (0.18 kg) and mounting are
  assumptions.
- **Head camera:** mounted pitched 45° down inside the head with a 75° vertical FOV. The neck's 19.5° downward
  limit cannot see the table on its own. This needs checking against the real camera mount.
- **Box task:** the carton is held by friction only (μ 0.5, no welds). It uses a 0.30–0.34 m carton because
  Asimov's fists only face each other in a narrow band, set by one-sided shoulder roll and a 5-DOF arm.

## Code

Simulation tasks, scripted experts, dataset export, GR00T modality config, closed-loop evaluation and the
one-shot training script: `asimov-groot` (to be published).

## License

Licensed by NVIDIA Corporation under the
[NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
This is a derivative of nvidia/GR00T-N1.7-3B, which uses nvidia/Cosmos-Reason2-2B, under the same license.
The Asimov robot model is from menloresearch/asimov-1 (GPL-2.0 software / CERN-OHL-S-2.0 hardware).