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act-aloha-insertion

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Mis à jour12 juillet 2026 à 15:35

S1 Vision-Language-Action policy. Capabilities: insert, pick, place on peg, socket. 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. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

Installation

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Source

OpenRAL

OpenRAL/openral

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Basé sur la classification professionnelle SOC

Développeurs de logicielsProfessions informatiques et mathématiques·SOC 15-1252

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5 fichiers

SKILL.md

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name	act-aloha-insertion
description	S1 Vision-Language-Action policy. Capabilities: insert, pick, place on peg, socket. 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. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.
metadata	{"openral_rskill":true,"schema_version":0.1,"rskill_id":"OpenRAL/rskill-act-aloha-aloha_insertion-fp32","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"act","embodiment_tags":["aloha"],"actions":["insert","pick","place"],"objects":["peg","socket"],"scenes":["tabletop"],"sensors_required":["rgb:observation.images.top"],"state_dim":14,"action_dim":14,"action_representation":"joint_positions","runtime":"pytorch","quantization":"fp32/pytorch","chunk_size":100,"latency_budget":{"per_chunk_ms":25},"license_code":"Apache-2.0","license_weights":"mit","weights_uri":"hf://lerobot/act_aloha_sim_insertion_human","source_repo":"hf://lerobot/act_aloha_sim_insertion_human","paper_url":"https://arxiv.org/abs/2304.13705"}

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.

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name	act-aloha-insertion
description	S1 Vision-Language-Action policy. Capabilities: insert, pick, place on peg, socket. 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. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.
metadata	{"openral_rskill":true,"schema_version":0.1,"rskill_id":"OpenRAL/rskill-act-aloha-aloha_insertion-fp32","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"act","embodiment_tags":["aloha"],"actions":["insert","pick","place"],"objects":["peg","socket"],"scenes":["tabletop"],"sensors_required":["rgb:observation.images.top"],"state_dim":14,"action_dim":14,"action_representation":"joint_positions","runtime":"pytorch","quantization":"fp32/pytorch","chunk_size":100,"latency_budget":{"per_chunk_ms":25},"license_code":"Apache-2.0","license_weights":"mit","weights_uri":"hf://lerobot/act_aloha_sim_insertion_human","source_repo":"hf://lerobot/act_aloha_sim_insertion_human","paper_url":"https://arxiv.org/abs/2304.13705"}

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

Capabilities

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

Why this is discovery-only

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.