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OpenRAL/rskill-act-aloha-aloha_transfer_cube-fp32

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rskill-act-aloha-alohatransfercube-fp32

OpenRAL rSkill — ACT (Action Chunking Transformer) finetuned on the ALOHA bimanual cube-transfer task, packaged for OpenRAL.

This package wraps `lerobot/act_aloha_sim_transfer_cube_human` with a rskill.yaml manifest that adds capability checking, license surfacing, latency budgets, and local registry integration. It does not copy model weights.

Upstream model

Field	Value
Source repo	`lerobot/act_aloha_sim_transfer_cube_human`
Paper	arxiv:2304.13705 — Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware (Zhao et al., 2023)
License	MIT
Parameters	~52 M (transformer encoder-decoder)
Action chunk	100
Benchmark	ALOHA bimanual cube-transfer (`gym-aloha`)

Note. The published checkpoint predates lerobot's PolicyProcessorPipeline migration and ships without normalisation buffers. See tests/sim/test_aloha_bimanual_act_aloha.py for the resulting numerical-contract caveats.

Supported robots

Robot	Embodiment tag	Status	Notes
ALOHA bimanual (Trossen) — `gym-aloha` MuJoCo	`aloha`, `lerobot`	✓ sim	14-DoF (2 × 7-DoF arms with parallel grippers)

Sensors required

Key	Type	Resolution	Format
`observation.images.top`	RGB camera	640 × 480	`float32`

ACT for ALOHA cube-transfer ships with a single top-down RGB stream. No wrist or third-person view.

Manifest summary

Field	Value
`name`	`OpenRAL/rskill-act-aloha-aloha_transfer_cube-fp32`
`version`	`0.1.0`
`license`	`mit`
`role`	`s1`
`embodiment_tags`	`aloha`, `lerobot`
`runtime` / `quantization.dtype`	`pytorch` / `fp32`
`weights_uri`	`hf://lerobot/act_aloha_sim_transfer_cube_human`
`latency_budget.per_chunk_ms`	25 ms (warm; bf16 autocast ≈ 12 ms on RTX 4070 Laptop)
`latency_budget.warmup_ms`	5 000 ms
`latency_budget.load_ms`	10 000 ms
`commercial_use_allowed`	`true`

Full schema: openral_core.RSkillManifest — python/core/src/openral_core/schemas.py.

Reproduction

bash

git clone https://github.com/OpenRAL/openral && cd OpenRAL
just bootstrap && uv sync --all-packages --group sim

# End-to-end via the canonical SimEnvironment config:
just sim-act-aloha
# which runs:
#     openral sim run --config scenes/benchmark/aloha_transfer_cube.yaml --rskill rskills/act-aloha --save-video

# Sim test (real gym-aloha MuJoCo with contact dynamics):
uv run pytest tests/sim/test_aloha_bimanual_act_aloha.py -v -m sim

License

This rSkill package (rskill.yaml, README.md) is MIT to match the upstream weights. Commercial use is allowed (commercial_use_allowed: true).