GR00T-N1.7-ALOHA-RightArm-Multitask

GR00T N1.7 — ALOHA Right Arm, Multi-Task

GR00T N1.7 3B finetuned on two ALOHA right-arm teleoperation datasets in a single multi-task run. The two tasks are separated at inference time by the language instruction, not by separate weights.

Tasks

Total: 300 episodes / 79,857 frames @ 15 fps. The two datasets are mixed proportionally to their size (≈47% drawer / 53% kitchen).

Embodiment / modality

Single 6-DoF arm + 1-DoF gripper, controlled in joint space, registered under EmbodimentTag.NEW_EMBODIMENT.

• video: front (observation.images.color.high), wrist (observation.images.color.wrist_right), 480x640
• state: single_arm (6), gripper (1) — raw observation.state[0:7]
• action: single_arm (relative, non-EEF), gripper (absolute, non-EEF), 16-step chunk
• language: annotation.human.task_description

The source datasets carry a 13-dim state/action vector: [0:6] arm joints, [6:7] gripper, [7:13] an end-effector pose block. This model uses the joint block only. The EEF block was deliberately excluded because its roll/yaw wrap at ±π (~0.4-1% of frames jump by ~2π between consecutive samples), which the default min/max action normalization turns into spurious targets.

Training

Trained with the Isaac GR00T N1.7 finetune pipeline.

Open-loop evaluation

Predicted vs. ground-truth action chunks, 4 trajectories per task, 300 steps, action horizon 16:

Caveat: neither dataset has a held-out split, so these trajectories were seen during training. The numbers show that the policy fits the demonstrations and that language conditioning selects the right behaviour per task — they are not a measure of generalization. Closed-loop evaluation on real hardware is required for that.

Usage

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

policy = Gr00tPolicy(
    model_path="twanghcmut/GR00T-N1.7-ALOHA-RightArm-Multitask",
    embodiment_tag=EmbodimentTag.NEW_EMBODIMENT,
    device="cuda",
)
action, _ = policy.get_action(observation)  # observation carries the task instruction

Serve it for a real robot client with:

python gr00t/eval/run_gr00t_server.py \
  --model-path twanghcmut/GR00T-N1.7-ALOHA-RightArm-Multitask \
  --embodiment-tag NEW_EMBODIMENT

Limitations

• Right arm only; single arm, joint-position control. Not a bimanual policy.
• Trained on two tasks in one fixed scene setup each — no evidence it transfers to new objects, new scenes, or new instruction phrasings.
• optimizer.pt and rng_state.pth are excluded from this repo, so it can be used for inference and further finetuning from the weights, but not to resume this exact run.