Instructions to use ZachGarner/microduck-headstand-backroll with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Microduck
How to use ZachGarner/microduck-headstand-backroll with Microduck:
# Replace SLOT with the slot specified in the model card (walk, stand, sitstand, ground_pick, kick_left, kick_right, roulade). sudo robotctl policy load SLOT ZachGarner/microduck-headstand-backroll
- Notebooks
- Google Colab
- Kaggle
Download evaluation.json from ZachGarner/microduck-headstand-backroll: direct link, hf CLI and curl.
- Browser
- Download file 2.68 kB
-
https://huggingface.co/ZachGarner/microduck-headstand-backroll/resolve/main/evaluation.json
- Command line
-
hf download hf://ZachGarner/microduck-headstand-backroll/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/ZachGarner/microduck-headstand-backroll/resolve/main/evaluation.json
2.68 kB
| { | |
| "task": "Mjlab-HeadstandBackrollLegsTogether-Flat-MicroDuck", | |
| "run": "ax1vgv8z", | |
| "checkpoint": "model_1999.pt", | |
| "checkpoint_sha256": "e94cd3256060f09004a3b6210545fdfbe29b5e64084df697d37677e8ae19e8fd", | |
| "seed": 0, | |
| "bucket": "hold", | |
| "episodes": 32, | |
| "steps": 298, | |
| "seconds": 5.96, | |
| "successes": 32, | |
| "median_time_to_goal_s": 0.52, | |
| "sampled_head_force_median_n": null, | |
| "sampled_head_force_max_n": null, | |
| "reset_count": 0, | |
| "tool_sha256": { | |
| "check32.py": "d70963e6c1dcc69e311e9c57041d4d3e3a236532790321bfc39b9c6f8e3d4f51", | |
| "eval_checkpoint.py": "bb12b02d3a1562b350a008bf6436a3adc4be1f187f8dbaae44efc9b24a60a893" | |
| }, | |
| "per_episode": [ | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.5 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.5 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.5 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.5 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.5 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.54 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| } | |
| ] | |
| } | |