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act-libero

النجوم٣

التفرعات٠

آخر تحديث١٢ يوليو ٢٠٢٦ في ١٥:٣٥

S1 Vision-Language-Action policy. Capabilities: pick, place, open, close on bowl, cup, drawer, object. ACT (Zhao et al., 2023) on HuggingFaceVLA/libero. ResNet-18, 4+1 enc/dec, latent VAE, chunk_size=100. Two 256x256 RGB (image / image2) + 8-D state + 7-D action; plain chunked replay. State layout matches openral_sim's LIBERO backend. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

التثبيت

التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.

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المصدر

OpenRAL

OpenRAL/openral

فتح مستودع GitHub عرض مستودعات المنشئ

تنزيل

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المهن ذات الصلةSOC

استنادا إلى تصنيف SOC المهني

مطوّرو البرمجياتمهن الحاسوب والرياضيات·SOC 15-1252

مستكشف الملفات

4 ملفات

SKILL.md

readonly

name	act-libero
description	S1 Vision-Language-Action policy. Capabilities: pick, place, open, close on bowl, cup, drawer, object. ACT (Zhao et al., 2023) on HuggingFaceVLA/libero. ResNet-18, 4+1 enc/dec, latent VAE, chunk_size=100. Two 256x256 RGB (image / image2) + 8-D state + 7-D action; plain chunked replay. State layout matches openral_sim's LIBERO backend. 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-franka_panda-libero-fp32","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"act","embodiment_tags":["franka_panda"],"actions":["pick","place","open","close"],"objects":["bowl","cup","drawer","object"],"scenes":["tabletop","kitchen"],"sensors_required":["rgb:observation.images.camera1","rgb:observation.images.camera2"],"state_dim":8,"action_dim":7,"action_representation":"delta_ee_6d_plus_gripper","runtime":"pytorch","quantization":"fp32/pytorch","chunk_size":100,"latency_budget":{"per_chunk_ms":100},"license_code":"Apache-2.0","license_weights":"apache-2.0","weights_uri":"hf://Deepkar/libero-test-act","source_repo":"hf://Deepkar/libero-test-act","paper_url":"https://arxiv.org/abs/2304.13705"}

act-libero — 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 (Zhao et al., 2023) on HuggingFaceVLA/libero. ResNet-18, 4+1 enc/dec, latent VAE, chunk_size=100. Two 256x256 RGB (image / image2) + 8-D state + 7-D action; plain chunked replay. State layout matches openral_sim's LIBERO backend.

Capabilities

Verbs: pick · place · open · close
Objects: bowl · cup · drawer · object
Scenes: tabletop · kitchen
Embodiments: franka_panda

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: apache-2.0 — 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-franka_panda-libero-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.

المزيد من هذا المستودع

نفس المستودع

rskill-internvla-n1-mobile-base-vln-nf4

OpenRAL/openral

S1 Vision-Language-Action policy. Capabilities: navigate, reach on room, hallway, door, kitchen, landmark. InternVLA-N1 / DualVLN — a dual-system Vision-Language Navigation foundation model (Qwen2.5-VL-7B waypoint planner + NavDP diffusion controller). Takes egocentric RGB-D plus a natural-language navigation instruction and drives a mobile base with body-twist velocity commands, emitting STOP on arrival. Zero-shot across wheeled and legged bases (paper: Unitree Go2 / H1). Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

2026-07-153

gr00t-n17-so101-fruit

OpenRAL/openral

S1 Vision-Language-Action policy. Capabilities: pick, place, pick_and_place, grasp on banana, orange, apple. GR00T N1.7 (3B, Cosmos-Reason2-2B backbone) community-finetuned on the SO-101 `so101_fruit` teleop dataset (200 eps, front+wrist RGB). Emits 6-D absolute joint chunks (5 arm + gripper) over a 16-step horizon under GR00T's `new_embodiment` tag. In-process lerobot GrootPolicy, whole-model NF4 — GPU-verified 5.8 GiB peak on 8 GB. NVIDIA Open Model License (commercial OK). Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

2026-07-123

3d-diffuser-actor-rlbench

OpenRAL/openral

S1 Vision-Language-Action policy. Capabilities: generalist, open, close, pick, place. 3D Diffuser Actor (Ke et al., 2024) — a diffusion policy over end-effector keyposes fusing multi-view RGB-D into a 3D scene representation, on the RLBench PerAct 18-task benchmark. Shares the out-of-process CoppeliaSim/PyRep sidecar with the rlbench scene backend. MIT code + checkpoints. The PerAct checkpoint is loaded verbatim; ships three live-verified starter tasks. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

2026-07-123

act-aloha-insertion

OpenRAL/openral

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.

2026-07-123

act-aloha

OpenRAL/openral

S1 Vision-Language-Action policy. Capabilities: transfer, pick, place on cube. Action Chunking Transformer (~52M-param encoder-decoder) finetuned on the ALOHA bimanual cube-transfer demonstration set. Action chunks of length 100. The mean/std norm buffers live inside model.safetensors; modern lerobot ACTPolicy drops them on load and the adapter re-applies them — see tests/sim/test_aloha_bimanual_act_aloha.py. Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

2026-07-123

act-so101-pen

OpenRAL/openral

S1 Vision-Language-Action policy. Capabilities: pick, place, pick_and_place, transfer on pen. ACT (Zhao et al., 2023) finetuned for "pass the pen" pick-and-place on a real SO-101 follower arm (Apache-2.0). ResNet-18 backbone, 4+1 enc/dec, latent VAE, chunk_size=100. Emits 6-DoF absolute joint-position chunks (in joint degrees) from two RGB views (front/overview + wrist). A smaller, faster, ONNX/TensorRT-friendly sibling of rskill-smolvla-so101-pen-bf16; the whole model exports to a single ONNX graph (OPENRAL_ACT_TRT=1). Discovery view of an OpenRAL rSkill — NOT directly runnable by an agent harness; it runs via rSkill.from_pretrained + the robot HAL.

2026-07-123

name	act-libero
description	S1 Vision-Language-Action policy. Capabilities: pick, place, open, close on bowl, cup, drawer, object. ACT (Zhao et al., 2023) on HuggingFaceVLA/libero. ResNet-18, 4+1 enc/dec, latent VAE, chunk_size=100. Two 256x256 RGB (image / image2) + 8-D state + 7-D action; plain chunked replay. State layout matches openral_sim's LIBERO backend. 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-franka_panda-libero-fp32","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"act","embodiment_tags":["franka_panda"],"actions":["pick","place","open","close"],"objects":["bowl","cup","drawer","object"],"scenes":["tabletop","kitchen"],"sensors_required":["rgb:observation.images.camera1","rgb:observation.images.camera2"],"state_dim":8,"action_dim":7,"action_representation":"delta_ee_6d_plus_gripper","runtime":"pytorch","quantization":"fp32/pytorch","chunk_size":100,"latency_budget":{"per_chunk_ms":100},"license_code":"Apache-2.0","license_weights":"apache-2.0","weights_uri":"hf://Deepkar/libero-test-act","source_repo":"hf://Deepkar/libero-test-act","paper_url":"https://arxiv.org/abs/2304.13705"}

act-libero — 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: pick · place · open · close
Objects: bowl · cup · drawer · object
Scenes: tabletop · kitchen
Embodiments: franka_panda

Why this is discovery-only

License

Code: Apache-2.0.
Weights: apache-2.0 — 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-franka_panda-libero-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.