gr00t-n17-so101-fruit

name	gr00t-n17-so101-fruit
description	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.
metadata	{"openral_rskill":true,"schema_version":0.1,"rskill_id":"OpenRAL/rskill-gr00t_n17-so101-fruit-bf16","manifest":"./rskill.yaml","role":"s1","kind":"vla","model_family":"gr00t","embodiment_tags":["so101_follower"],"actions":["pick","place","pick_and_place","grasp"],"objects":["banana","orange","apple"],"scenes":["tabletop"],"sensors_required":["rgb:observation.images.camera1","rgb:observation.images.camera2"],"state_dim":6,"action_dim":6,"action_representation":"joint_positions","runtime":"pytorch","quantization":"bf16/pytorch","min_vram_gb":{"bf16":12,"int4":6},"chunk_size":16,"latency_budget":{"per_chunk_ms":1500},"license_code":"Apache-2.0","license_weights":"nvidia_open_model","weights_uri":"hf://aaronsu11/GR00T-N1.7-3B-SO101-FruitPicking@26b179c37f35168359ccdf2e51fed6c2690186cf","source_repo":"hf://aaronsu11/GR00T-N1.7-3B-SO101-FruitPicking","paper_url":"https://arxiv.org/abs/2503.14734"}

gr00t-n17-so101-fruit — 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). 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).

Capabilities

• Verbs: pick · place · pick_and_place · grasp
• Objects: banana · orange · apple
• Scenes: tabletop
• Embodiments: so101_follower

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: nvidia_open_model — 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-gr00t_n17-so101-fruit-bf16")
# 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.