---
base_model: nvidia/GR00T-N1.6-3B
pipeline_tag: robotics
tags:
  - robotics
  - vla
  - gr00t
  - r1-lite
  - end-effector-control
---

# VLASH GR00T N1.6 — R1 Lite, EE-pose actions (random delay 0–4)

Finetune of Isaac GR00T N1.6 for the Galaxea R1 Lite embodiment that outputs **end-effector pose
actions instead of arm joint actions**, 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.

## Modalities

| | keys | dim |
|---|---|---|
| Video | `head_rgb`, `left_wrist_rgb`, `right_wrist_rgb` | 3 views, 1 frame |
| State | `left_arm`, `right_arm`, `left_gripper`, `right_gripper`, `left_ee_pose`, `right_ee_pose` | 6+6+1+1+9+9 = **32** |
| Action | `left_gripper`, `right_gripper`, `left_ee_pose`, `right_ee_pose` | 1+1+9+9 = **20** |

EE poses are `xyz + rot6d` (`ActionFormat.XYZ_ROT6D`), where `rot6d` is the first two rows of the
rotation matrix — 9 numbers per arm, not a 6-DoF pose vector. EE actions are **relative**
(`ActionRepresentation.RELATIVE`, `ActionType.EEF`) against the corresponding EE state as the
reference frame; grippers are absolute on a 0–100 scale.

## Details

| | |
|---|---|
| Base model | Isaac GR00T N1.6 (`nvidia/GR00T-N1.6-3B`) |
| Checkpoint | step 160000 |
| Embodiment tag | `new_embodiment` (R1 Lite, bimanual + 2 grippers) |
| Action chunk (training) | 32 |
| `max_delay_steps` | 4 (random per sample, `k ~ U[0, 4]`) |
| `use_state_ground_truth` | true (delayed state is the *measured* state at `t + k`) |
| Global batch size / steps | 32 / 160000 |
| Dataset | R1 Lite `foldhoodie`, 50 episodes, 72474 frames @ 31 fps |

## Open-loop evaluation

Delay sweep with `gr00t/eval/delayed_open_loop_eval.py`, 3 trajectories × 800 steps, horizon 16.
Unnormalized error, averaged over trajectories.

EE pose MSE (metres / rot6d units):

| delay | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
|---|---|---|---|---|---|---|---|---|---|
| left | 5.0e-5 | 4.4e-5 | **4.3e-5** | 4.9e-5 | 4.8e-5 | 5.3e-5 | 6.0e-5 | 6.8e-5 | 7.2e-5 |
| right | 6.1e-5 | **6.1e-5** | 6.3e-5 | 7.3e-5 | 7.8e-5 | 8.5e-5 | 9.8e-5 | 1.04e-4 | 1.13e-4 |
| mean | 5.5e-5 | **5.2e-5** | 5.3e-5 | 6.1e-5 | 6.3e-5 | 6.9e-5 | 7.9e-5 | 8.6e-5 | 9.2e-5 |

EE MAE at the minimum is 3.2e-3 (left) / 4.3e-3 (right), i.e. a few millimetres against an xyz
range of roughly 0.3–0.45 m.

EE error bottoms out at **delay 1–2**, which is the mean of the `U[0, 4]` training distribution,
and rises monotonically out to delay 8 (about 1.8× the minimum). Gripper error is uncorrelated
with delay — those are near-binary 0/100 transitions where a single mistimed frame swamps the
metric, and the between-trajectory spread (right gripper: ~30 on one trajectory vs ~250 on
another) is far larger than any delay effect. The concatenated 20-dim MSE is dominated by the
grippers for the same reason, so use the EE rows to compare delays.

**Caveats:** the sweep was run on the training dataset, so these are train-set fit, not held-out
generalization. Also, the EE action labels in this dataset are the *measured* EE pose at `t + 1`
(a state-shifted pseudo-action), not the controller's commanded target, so they carry no tracking
error or actuation lag.

## 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-ee-gr00t-n1.6-160000",
    device="cuda",
)
action_chunk, _ = policy.get_action(observation)
# action_chunk keys: left_gripper, right_gripper, left_ee_pose, right_ee_pose
```

Because of the delay augmentation, the returned chunk is meant to start executing a few steps
after the observation timestamp rather than immediately. The EE outputs are absolute poses in the
same frame as `observation.state.*_ee_pose_9d`; a downstream IK step is required to drive the
arms, which the joint-action variant
([`XYZPIT/vlash-random4-gr00t-n1.6-160000`](https://huggingface.co/XYZPIT/vlash-random4-gr00t-n1.6-160000))
does not need.