rskill-openvla_oft-widowx-simpler_env-nf4

rskill-openvla_oft-widowx-simpler_env-nf4

OpenVLA-OFT bridge policy (RLinf, PPO-tuned on ManiSkill3 PutOnPlateInScene25), packaged for OpenRAL and locally verified on SimplerEnv WidowX carrot-on-plate.

What this skill does

Wraps RLinf/RLinf-OpenVLAOFT-PPO-ManiSkill3-25ood — an OpenVLA-OFT (arXiv:2502.19645) policy, RL-tuned with PPO on the ManiSkill3 PutOnPlateInScene25 task using a WidowX 250 S — and runs it on the SimplerEnv WidowX (Bridge V2) carrot-on-plate task that shares its embodiment, EE-delta control, and bridge_orig normalization. The sibling Bridge tasks are not declared in evaluated_tasks until locally reproduced.

Why WidowX and not Panda/PickCube: this checkpoint is a bridge policy. The ManiSkill3 Panda PickCube-v1 scenes are a different embodiment and task; the task-data gate correctly refuses that pairing (it would produce a plausible-but-unsolvable rollout). See the OpenVLA / OpenVLA-OFT policy family notes below for the full rationale.

How it works

Loaded in-process by OpenRAL's openvla policy adapter (python/sim/src/openral_sim/policies/openvla.py) as a transformers custom-code model (AutoModelForVision2Seq + trust_remote_code, gated by OPENRAL_ALLOW_REMOTE_CODE=1). NF4 (4-bit) quantization plus the CUDA expandable-segments allocator bring the 7.5 B backbone within an 8 GB GPU. The RLinf checkpoint currently needs a 4.40-era transformers runtime; the default OpenRAL workspace pins transformers 5.3 for lerobot families, so keep OpenVLA validation in a dedicated environment rather than syncing it together with the default VLA groups.

Observation → action contract

• Input: one 224×224 RGB frame (the SimplerEnv 3rd-view, surfaced as camera1) and the prompt In: What action should the robot take to {instruction.lower()}?\nOut: . No proprioception (use_proprio=False).
• Output: 256-bin discrete action tokens decoded to [-1, 1], then de-normalized with the embedded unnorm_key=bridge_orig stats (BOUNDS_Q99): 6 end-effector deltas (3 position + 3 rotation) rescaled, gripper passed through. The manifest drives RLinf's generate_action_verl path with right-padded prompts (max_length=30), temperature sampling (0.6), torch seed 0, action_scale=2.0 on the first six dimensions, and binary gripper threshold 0.5. Action chunk = 8 × 7-D, replayed open-loop.

Upstream model / training

Upstream base Haozhan72/Openvla-oft-SFT-libero-goal-trajall, ManiSkill LoRA SFT, then PPO on PutOnPlateInScene25Main-v3 (WidowX 250 S). RLinf model-index success: train 0.977; OOD vision 0.921 / semantic 0.648 / position 0.736. See the upstream card for the full protocol.

Supported robots

• widowx (WidowX 250 S, Bridge V2 flat-table setup).

Sensors required

• One RGB stream, ≥224×224, mapped to observation.images.camera1.

Manifest summary

See rskill.yaml. Key fields: model_family: openvla, license: mit, quantization.dtype: int4, chunk_size: 8, evaluated_tasks = simpler_env/widowx_carrot_on_plate, benchmarks.simpler_env_widowx: 0.4, policy_extras = the RLinf generation and action-transform knobs, action_contract = delta_ee_6d_plus_gripper (dim 7).

Quick start

just sync --all-packages --group simpler-env
hf download RLinf/RLinf-OpenVLAOFT-PPO-ManiSkill3-25ood
OPENRAL_ALLOW_REMOTE_CODE=1 openral benchmark run \
  --suite simpler_env_widowx --task simpler_env/widowx_carrot_on_plate \
  --rskill openvla-oft-simpler-widowx-nf4

Reproduction

# Single SimplerEnv WidowX scene (carrot-on-plate):
OPENRAL_ALLOW_REMOTE_CODE=1 openral benchmark run \
  --suite simpler_env_widowx --task simpler_env/widowx_carrot_on_plate \
  --rskill openvla-oft-simpler-widowx-nf4

Evaluation

Local seeded validation on an RTX 4070 Laptop GPU (8 GB), NF4, SimplerEnv ManiSkill3 PutCarrotOnPlateInScene-v1, 5 episodes, seeds 0..4, 60-step horizon, generate_action_verl, torch seed 0 reapplied on each policy reset, action_scale=2.0:

• simpler_env/widowx_carrot_on_plate: 2/5 success (40%).

Public widowx_carrot_on_plate without the RLinf action transform scored 0/5, and the exact upstream PutOnPlateInScene25Main-v3 registration needs RLinf assets that were not present in the public source checkout. Those numbers are not claimed here.

License

MIT (upstream RLinf/RLinf-OpenVLAOFT-PPO-ManiSkill3-25ood). The OpenRAL packaging is Apache-2.0. The checkpoint is a trust_remote_code custom-code model; loading executes repo-shipped Python and requires OPENRAL_ALLOW_REMOTE_CODE=1 (provenance: rSkill signature verification is not yet implemented).

See also

• OpenVLA / OpenVLA-OFT policy family
• benchmark task-data compatibility gate
• rldx1-ft-simpler-widowx-nf4 — the sibling WidowX bridge rSkill.