Instructions to use jaeikkim/fr3-cube-full10k-diffusion-policy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use jaeikkim/fr3-cube-full10k-diffusion-policy with LeRobot:
- Notebooks
- Google Colab
- Kaggle
FR3 Cube Full10K Diffusion Policy
Evaluation-ready LeRobot Diffusion Policy checkpoints from cumulative optimizer step 10K through 50K at 2.5K intervals, plus the 60K checkpoint.
Policy
- Observation horizon: 2
- Prediction horizon: 16
- Action chunk (
n_action_steps): 8 - Three native 320×180 RGB views + 22D robot state
- 7D relative Cartesian OSC action
- Conditional 1D U-Net with ResNet18 visual encoder
Each checkpoint is a LeRobot pretrained_model/ directory containing the model config, preprocessing/postprocessing state, and model.safetensors. Load the directory directly with DiffusionPolicy.from_pretrained(...) or the UWLab rollout evaluator.
Checkpoint provenance
The continuation restored model weights exactly, but the original W&B artifact did not contain optimizer/scheduler state. Therefore these releases are evaluation-ready model checkpoints, not full-state training-resume checkpoints. The cumulative step is encoded in each directory name.
Download example
hf download jaeikkim/fr3-cube-full10k-diffusion-policy checkpoints/step_050000/pretrained_model --local-dir ./fr3_cube_dpu
Dataset: FR3 Cube Full-Episode RL-Teacher 10K RGB. Verify model files with SHA256SUMS or checkpoint_manifest.json.