Instructions to use OpenRAL/rskill-act-aloha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use OpenRAL/rskill-act-aloha with LeRobot:
- Notebooks
- Google Colab
- Kaggle
| # `rskills/act-aloha/eval/` — benchmark results | |
| `aloha_transfer_cube.json` is the ALOHA bimanual cube-transfer benchmark | |
| result block for this rSkill. Validated against | |
| [`openral_core.RSkillEvalResult`](../../../docs/reference/schemas/RSkillEvalResult.json) | |
| at load time by the `rSkill` loader and surfaced by `openral benchmark report`. | |
| | Field | Value | | |
| | --- | --- | | |
| | Source | Zhao et al., 2023 — *Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware* (arxiv:2304.13705) | | |
| | Benchmark | ALOHA bimanual cube transfer (`gym_aloha/AlohaTransferCube-v0`) | | |
| | Robot | Trossen ALOHA (2 × 7-DoF + parallel grippers, 14-DoF action) | | |
| | Reproduced locally? | ✗ — paper-only. `tests/sim/test_aloha_bimanual_act_aloha.py` runs a single episode for IO + latency verification but does not aggregate the 50-trial protocol. | | |
| | Reproduce | `just sim-act-aloha` (single episode); raise `--n-episodes 50` for the full paper protocol. | | |