CoRL2026-CSI/Gr00t_n1.5-IsaacLab-SO101-Multi_Task-30fps_8epoch

CoRL2026-CSI/IsaacLab-so101-multi-gr00t

This is a LeRobot GR00T N1.5 policy fine-tuned from nvidia/GR00T-N1.5-3B on CoRL2026-CSI/Isaaclab-so101_11task_baseCaP_3300epi.

The model is intended for SO-101 style manipulation experiments using RGB observations, robot proprioception, language instructions, and continuous action chunks. It is uploaded as a LeRobot policy checkpoint and should be loaded through the matching LeRobot GR00T implementation used for training.

Model Details

• Policy type: GR00T N1.5
• Base model: nvidia/GR00T-N1.5-3B
• Tokenizer assets: lerobot/eagle2hg-processor-groot-n1p5
• Embodiment tag: new_embodiment
• Observation steps: 1
• Action chunk size: 16
• Action steps: 16
• Max state/action dims: 64 / 32

Fine-Tuning Setup

• Training steps: 110000
• Approx. epochs: 7.99
• Final training samples: 28160000
• Final training loss: 0.010196
• Runtime: 76.72 hours
• Micro batch size: 64
• Gradient accumulation steps: 2
• Effective batch size: 256
• Optimizer: adamw, lr 0.0001, weight decay 1e-05
• Scheduler: cosine_decay_with_warmup
• Mixed precision: bf16 policy weights/config enabled: true
• VLM/LLM backbone fine-tuned: false
• Vision tower fine-tuned: false
• Action projector fine-tuned: true
• Action DiT fine-tuned: true

The GR00T vision-language backbone was frozen for this run. The action head projector and flow-matching diffusion transformer were fine-tuned.

Inputs

• observation.ee_pos.robot_xyzrpy: STATE, shape [6]
• observation.gripper_binary: STATE, shape [1]
• observation.images.left_wrist: VISUAL, shape [3, 480, 640]
• observation.images.top: VISUAL, shape [3, 480, 640]
• observation.state: STATE, shape [6]
• observation.state.radian_urdf0: STATE, shape [6]

Outputs

• action: ACTION, shape [6]
• action.radian_urdf0: ACTION, shape [6]

Usage

Install and use the same LeRobot checkout/environment that contains the GR00T policy implementation, then point policy.path to this Hub repo.

lerobot-record \
  --robot.type=<your_robot> \
  --dataset.repo_id=<your_eval_dataset_repo> \
  --policy.path=CoRL2026-CSI/IsaacLab-so101-multi-gr00t \
  --episodes=10

For local Python usage, load the policy with LeRobot's policy factory or GR00T policy loader from the training checkout.

Evaluation

This upload records the offline training run metrics only. No rollout success rate is claimed here unless a separate real/sim evaluation is added later.

Final logged training metrics:

• loss: 0.010196
• grad norm: 0.193945
• update time: 1.2521 s/step
• dataloading time: 0.0046 s/step

Limitations and Safety

This model is a robot control policy and can produce unsafe actions if deployed on hardware without appropriate validation, workspace limits, emergency stop handling, and task-specific safety checks. Test in simulation or a constrained setup before any physical deployment.

The model is specialized to the training dataset and embodiment configuration. It may not transfer reliably to different cameras, calibration, action spaces, robot hardware, or tasks without further validation or fine-tuning.

License and Terms

This model is a fine-tune of nvidia/GR00T-N1.5-3B; users are responsible for complying with the NVIDIA model license and any dataset/license constraints. See the base model card and NVIDIA license terms linked in the metadata.

Files

• model.safetensors: fine-tuned policy weights
• config.json: LeRobot GR00T policy config
• train_config.json: training configuration
• policy_preprocessor.json and policy_postprocessor.json: LeRobot processor pipelines
• policy_*_step_*.safetensors: normalization/statistics state used by processors