README.md

metadata

language:
  - en
library_name: lerobot
pipeline_tag: robotics
tags:
  - vision-language-action
  - imitation-learning
  - lerobot
  - pi05
  - manipulation
inference: false
datasets:
  - PickNikRobotics/kinova_gen3_cube_stack_sim
base_model:
  - lerobot/pi05_base
license: gemma
extra_gated_heading: Access this model on Hugging Face
extra_gated_prompt: >-
  This model is a Gemma Model Derivative and is provided under and subject to
  the Gemma Terms of Use (ai.google.dev/gemma/terms). Its use is restricted by
  the Gemma Prohibited Use Policy (ai.google.dev/gemma/prohibited_use_policy).
  To access it, review and agree to those terms.
extra_gated_button_content: Acknowledge license

pi0.5: Kinova Gen3 cube stacking

Color-conditioned cube stacking on a Kinova Gen3. Given a prompt such as stack the red cube on the green cube, the policy picks the named cube and places it on the named target.

License

Gemma Terms of Use. model.safetensors contains modified Gemma weights, derived from pi0.5's PaliGemma backbone and Gemma action expert, and is distributed with LICENSE_GEMMA.txt and NOTICE.

Use is restricted by the Gemma Prohibited Use Policy. Pass these terms on to anyone you redistribute the weights to.

Model description

• Inputs: three 480x480 RGB views keyed scene, wrist, and overview, an 8-dimensional joint state, and a language instruction
• Outputs: 8-dimensional joint position targets: 7 Kinova Gen3 arm joints and the Robotiq 2F-85 gripper
• Training objective: flow matching
• Control rate: 10 Hz, action chunks of 50

Serving needs --state-dim 8. config.json declares observation.state as [32], pi0.5's padded architecture width, while the normalizer statistics are 8-wide.

Training

lerobot-train, pi0.5 LoRA-backbone: LoRA (r=32, alpha=64) on the PaliGemma language model's attention and MLP projections, dense training for the action expert and projection heads, SigLIP frozen. 50,000 steps, batch 4, lr 2.5e-5 cosine, weight decay 0.01, seed 1000, bfloat16. See train_config.json for the full configuration.

Trained on kinova_gen3_cube_stack_sim, 360 episodes across all six color-ordered prompts.

Evaluation

150 held-out layouts in MuJoCo, real-time chunking on.

gate	rate
pick, the named cube grasped and lifted	94.0%
stack, placed on the named target	81.3%