openral/openral/act-aloha-insertion

act-aloha-insertion — rSkill discovery view

Generated view, not a hand-written skill. This SKILL.md is a discovery-only

mirror of `rskill.yaml`, produced by tools/generate_rskill_skillmd.py.

It lets tools that read the standard agent-skill format find and reason about this

OpenRAL rSkill. The rskill.yaml manifest is the single source of truth

(CLAUDE.md §1.3). Do not edit by hand — edit the manifest and regenerate.

What it is

An OpenRAL Vision-Language-Action policy (role: s1, kind: vla). ACT (~52M params, chunk=100) finetuned on the ALOHA bimanual sim-insertion demonstration set. Insertion is the harder ALOHA task in the original paper; the 0.20 success rate reflects that. See the norm-stats note above re: the legacy safetensors-resident buffers.

Capabilities

• Verbs: insert · pick · place
• Objects: peg · socket
• Scenes: tabletop
• Embodiments: aloha

Why this is discovery-only

An agent skill is natural-language instructions loaded into an LLM's context. An rSkill is an executable artifact: it carries a typed capability/embodiment contract, model weights, a runtime, and a license/provenance gate — none of which fit in freeform markdown. So an agent can use this view to select the right skill, but cannot execute it by loading this file. Execution always goes through the OpenRAL loader and the robot HAL.

License

• Code: Apache-2.0.
• Weights: mit — permissive / commercial-use OK

How to actually run it (not via an agent harness)

from openral_rskill import rSkill
skill = rSkill.from_pretrained("OpenRAL/rskill-act-aloha-aloha_insertion-fp32")
# the loader validates embodiment / sensors / runtime / quantization against the target
# RobotDescription and enforces the weight-license gate before any weights load.

See `rskill.yaml` for the authoritative, validated manifest.