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pi0_so101_libero_finetune – AI Model by SilverKittyy | AlphaNeural AI

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pi0_so101_libero_finetune

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π₀ SO-101 Libero Fine-tuned Model

This is a π₀ (pi-zero) model fine-tuned on the Libero dataset for manipulation tasks with the SO-101 follower arm.

Model Details

Model Type: π₀ (Physical Intelligence Zero)
Robot Type: SO-101 Follower Arm
Dataset: Libero
Task: Manipulation (pick and place, object manipulation)
Input: RGB images (224x224) + robot state (32-dim)
Output: Robot actions (7-dim)

Usage with LeRobot

Recording with Policy

bash

lerobot-record \
  --robot.type=so101_follower \
  --robot.port=/dev/ttyACM0 \
  --robot.cameras='{"up":{"type":"opencv","index_or_path":0,"width":640,"height":480,"fps":30}}' \
  --robot.id=my_awesome_follower_arm \
  --dataset.single_task="pick up the screwdriver" \
  --policy.path=SilverKittyy/pi0_so101_libero_finetune \
  --dataset.repo_id=local_eval \
  --dataset.num_episodes=1

Evaluation

bash

lerobot-eval \
  --policy.path=SilverKittyy/pi0_so101_libero_finetune \
  --dataset.repo_id=your_evaluation_dataset \
  --eval.n_episodes=10

Model Architecture

Vision Encoder: Frozen vision encoder
State Processing: 32-dimensional state vector
Action Space: 7-dimensional (6 joint positions + gripper)
Normalization: Mean-std normalization for all inputs/outputs

Training Details

Base Model: π₀ pre-trained model
Fine-tuning Dataset: Libero manipulation tasks
Training Steps: 29,999
Learning Rate: 2.5e-05
Optimizer: AdamW with cosine decay

Hardware Requirements

Robot: SO-101 Follower Arm
Camera: OpenCV-compatible camera (e.g., /dev/video0)
Compute: CUDA-capable GPU recommended

Safety Notes

This model is trained for manipulation tasks
Always ensure proper safety measures when using with real hardware
Test in simulation or with safety constraints before real-world deployment