Instructions to use ZachGarner/microduck-headstand-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Microduck
How to use ZachGarner/microduck-headstand-split 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-split
- Notebooks
- Google Colab
- Kaggle
Download evaluation.json from ZachGarner/microduck-headstand-split: direct link, hf CLI and curl.
- Browser
- Download file 2.69 kB
-
https://huggingface.co/ZachGarner/microduck-headstand-split/resolve/main/evaluation.json
- Command line
-
hf download hf://ZachGarner/microduck-headstand-split/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/ZachGarner/microduck-headstand-split/resolve/main/evaluation.json
2.69 kB
| { | |
| "task": "Mjlab-HeadstandKickup-Flat-MicroDuck", | |
| "run": "y2fllvgj", | |
| "checkpoint": "model_1499.pt", | |
| "checkpoint_sha256": "66387c82f6f070f1d5990aeb811ae5fd7dbd9a2213f496bda2eb17070824a444", | |
| "seed": 0, | |
| "bucket": "handover", | |
| "episodes": 32, | |
| "steps": 298, | |
| "seconds": 5.96, | |
| "successes": 32, | |
| "median_time_to_goal_s": 0.42, | |
| "sampled_head_force_median_n": 9.054665565490723, | |
| "sampled_head_force_max_n": 17.901735305786133, | |
| "reset_count": 0, | |
| "tool_sha256": { | |
| "check32.py": "d70963e6c1dcc69e311e9c57041d4d3e3a236532790321bfc39b9c6f8e3d4f51", | |
| "eval_checkpoint.py": "bb12b02d3a1562b350a008bf6436a3adc4be1f187f8dbaae44efc9b24a60a893" | |
| }, | |
| "per_episode": [ | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.44 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.38 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.46 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.44 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.44 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.38 | |
| } | |
| ] | |
| } | |