rskill-act-aloha

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
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

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/benchmarks/act_aloha_transfer_cube.yaml --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).

See also

• robots/aloha_bimanual/README.md — RobotDescription manifest.
• scenes/benchmarks/act_aloha_transfer_cube.yaml — paired SimEnvironment config.
• docs/reference/vla_compatibility.md — VLA × Robot × Sim matrix.