Instructions to use masondx/new_ring_rot_obs_local with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use masondx/new_ring_rot_obs_local with LeRobot:
- Notebooks
- Google Colab
- Kaggle
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Download README.md from masondx/new_ring_rot_obs_local: direct link, hf CLI and curl.
- Browser
- Download file 1.66 kB
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https://huggingface.co/masondx/new_ring_rot_obs_local/resolve/main/README.md
- Command line
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hf download hf://masondx/new_ring_rot_obs_local/README.md
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curl -L -o README.md https://huggingface.co/masondx/new_ring_rot_obs_local/resolve/main/README.md
1.66 kB
metadata
datasets: masondx/new_ring_rot_xyz_obs
library_name: lerobot
license: apache-2.0
model_name: diffusion
pipeline_tag: robotics
tags:
- lerobot
- diffusion
- robotics
Model Card for diffusion
Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation.
This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs.
How to Get Started with the Model
For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval:
Train from scratch
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.
Evaluate the policy/run inference
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.
Model Details
- License: apache-2.0