Instructions to use ZachGarner/microduck-headstand-fold with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Microduck
How to use ZachGarner/microduck-headstand-fold 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-fold
- Notebooks
- Google Colab
- Kaggle
Download evaluation.json from ZachGarner/microduck-headstand-fold: direct link, hf CLI and curl.
- Browser
- Download file 2.7 kB
-
https://huggingface.co/ZachGarner/microduck-headstand-fold/resolve/main/evaluation.json
- Command line
-
hf download hf://ZachGarner/microduck-headstand-fold/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/ZachGarner/microduck-headstand-fold/resolve/main/evaluation.json
2.7 kB
| { | |
| "task": "Mjlab-HeadstandFold-Flat-MicroDuck", | |
| "run": "vorty4kb", | |
| "checkpoint": "model_1000.pt", | |
| "checkpoint_sha256": "82a97cbc871c718f82a467682c014555f64c78e9e9ce33438a2bc3a75636eec5", | |
| "seed": 0, | |
| "bucket": "standing", | |
| "episodes": 32, | |
| "steps": 298, | |
| "seconds": 5.96, | |
| "successes": 32, | |
| "median_time_to_goal_s": 0.4, | |
| "sampled_head_force_median_n": 11.305006980895996, | |
| "sampled_head_force_max_n": 34.81301498413086, | |
| "reset_count": 0, | |
| "tool_sha256": { | |
| "check32.py": "d70963e6c1dcc69e311e9c57041d4d3e3a236532790321bfc39b9c6f8e3d4f51", | |
| "eval_checkpoint.py": "bb12b02d3a1562b350a008bf6436a3adc4be1f187f8dbaae44efc9b24a60a893" | |
| }, | |
| "per_episode": [ | |
| { | |
| "success": true, | |
| "first_goal_s": 0.56 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.36 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.46 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.48 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.48 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.62 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.36 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.36 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.6 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.38 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.36 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.34 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.58 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.42 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.62 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.4 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.44 | |
| }, | |
| { | |
| "success": true, | |
| "first_goal_s": 0.52 | |
| } | |
| ] | |
| } | |