Robotics
LeRobot
English
OpenRAL
rskill
diffusion
vision-language-action
pusht
diffusion-policy
manipulation
Instructions to use OpenRAL/rskill-diffusion-pusht-pusht-fp32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use OpenRAL/rskill-diffusion-pusht-pusht-fp32 with LeRobot:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
|
Download eval/README.md from OpenRAL/rskill-diffusion-pusht-pusht-fp32: direct link, hf CLI and curl.
- Browser
- Download file 880 Bytes
-
https://huggingface.co/OpenRAL/rskill-diffusion-pusht-pusht-fp32/resolve/main/eval/README.md
- Command line
-
hf download hf://OpenRAL/rskill-diffusion-pusht-pusht-fp32/eval/README.md
-
curl -L -o README.md https://huggingface.co/OpenRAL/rskill-diffusion-pusht-pusht-fp32/resolve/main/eval/README.md
880 Bytes
rskills/diffusion-pusht/eval/ — benchmark results
pusht.json is the PushT mean-coverage-IoU benchmark result block for this
rSkill. Validated against
openral_core.RSkillEvalResult
at load time by the rSkill loader and surfaced by openral benchmark report.
| Field | Value |
|---|---|
| Source | Chi et al., 2023 — Diffusion Policy: Visuomotor Policy Learning via Action Diffusion (arxiv:2303.04137) |
| Benchmark | PushT (gym_pusht/PushT-v0, pymunk 2-D rigid-body) |
| Robot | PushT 2-D pseudo-robot (single 2-D end-effector tip) |
| Reproduced locally? | ✗ — paper-only. tests/sim/test_pusht_2d_diffusion_pusht.py runs a single episode for IO + latency + VRAM verification. |
| Reproduce | just sim-diffusion-pusht (single episode); raise --n-episodes 50 for the full paper protocol. |