Spot Robot GR00T Model
This is a fine-tuned GR00T model for Spot robot control, specifically trained for quadruped locomotion tasks.
Model Details
- Base Model: GR00T-N1.5-3B
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Task: Spot robot action generation
- Input Modalities: Video, State, Language
- Output: Robot joint actions (4 legs × 3 joints each)
Usage
from gr00t.model.policy import Gr00tPolicy
from gr00t.data.embodiment_tags import EmbodimentTag
# Load the model
policy = Gr00tPolicy(
model_path="namaewa-im/spot-gr00t-task0",
embodiment_tag=EmbodimentTag.NEW_EMBODIMENT,
device="cuda"
)
# Use for inference
actions = policy.get_action(input_data)
Training Data
The model was fine-tuned on Spot robot demonstration data including:
- Video observations from ego-centric camera
- Joint positions and velocities
- Linear and angular velocities
- Gravity and command information
- Human-annotated task descriptions
Performance
The model achieves competitive performance on Spot robot locomotion tasks with:
- Mean MSE: ~0.55
- Standard deviation: ~0.12
License
This model is released under the MIT License.
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nvidia/GR00T-N1.5-3B