---
license: other
library_name: gr00t
tags:
  - robotics
  - vla
  - gr00t
  - isaac-gr00t
  - r1-lite
pipeline_tag: robotics
---

# VLASH GR00T N1.6 — R1 Lite (random delay 0–4)

Finetune of Isaac GR00T N1.6 for the Galaxea R1 Lite embodiment, trained with
**VLASH temporal delay augmentation**: for every sample a delay `k ~ U[0, 4]` is drawn and
the state/action windows are shifted by `k` while video and language stay at the base timestep.

```
video/language:  t                                   (stale observation)
state:           t + k
action chunk:    action[t + k : t + k + horizon]
```

The policy therefore predicts an action chunk that begins `k` steps *after* the observation it
was given, which is what makes asynchronous chunk execution possible on the real robot: the next
chunk can be computed while the current one is still executing.

## Details

| | |
|---|---|
| Base model | Isaac GR00T N1.6 |
| Checkpoint | step 160000 |
| Embodiment tag | `new_embodiment` (R1 Lite, bimanual + 2 grippers) |
| Action horizon | 16 |
| `max_delay_steps` | 4 (random per sample) |
| `use_state_ground_truth` | true (delayed state is the *measured* state at `t + k`) |
| Tuned layers | backbone LLM layers 12–15 + action head |

## Open-loop evaluation

Delay sweep with `gr00t/eval/delayed_open_loop_eval.py`, 3 trajectories × 200 steps, horizon 16.
Unnormalized action error, averaged over trajectories.

Arm joints only (rad):

| delay | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|
| MSE | .00103 | .00124 | .00097 | .00049 | .00037 | **.00034** | .00036 | .00048 | .00048 |
| MAE | .01344 | .01358 | .01227 | .01129 | .01022 | **.01033** | .01071 | .01181 | .01196 |

All keys concatenated (dominated by the grippers, which span 0–100):

| delay | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|
| MSE | .00605 | .00619 | .00626 | .00561 | .00586 | **.00551** | .00571 | .00599 | .00629 |
| MAE | .02768 | .02792 | .02728 | .02535 | .02599 | **.02520** | .02588 | .02649 | .02739 |

Arm error bottoms out around delay 4–6 — roughly 3× lower than evaluating at delay 0 — matching the
0–4 training distribution, and degrades again at delay 7–8 (outside the trained range). Gripper
error is flat across delays: those are discrete open/close transitions that a few steps of shift
do not fix.

**Caveat:** this sweep was run on the training dataset, so the numbers are train-set fit, not
held-out generalization.

## Files

Inference artifacts only — `optimizer.pt`, `scheduler.pt`, `rng_state_*.pth`, and the trainer
state are not included, so this checkpoint cannot be used to resume training.

## Usage

```python
from gr00t.policy.gr00t_policy import Gr00tPolicy
from gr00t.data.embodiment_tags import EmbodimentTag

policy = Gr00tPolicy(
    embodiment_tag=EmbodimentTag.NEW_EMBODIMENT,
    model_path="XYZPIT/vlash-random4-gr00t-n1.6-160000",
    device="cuda",
)
action_chunk, _ = policy.get_action(observation)
```

Because of the delay augmentation, the returned chunk is meant to start executing a few steps
after the observation timestamp rather than immediately.