Tron-Ann/pi0-so101-test

Model Card for pi0

built in gcp,docker.

  --dataset.repo_id=Tron-Ann/so101-transfer-cube \
  --policy.type=pi0 \
  --policy.pretrained_path=lerobot/pi0_base \
  --policy.repo_id=Tron-Ann/pi0-so101-test \
  --policy.dtype=bfloat16 \
  --policy.gradient_checkpointing=true \
  --output_dir=outputs/pi0_smoketest \
  --job_name=pi0_smoketest \
  --wandb.enable=false \
  --steps=100 \
  --batch_size=4 \
  --policy.device=cuda```

**π₀ (Pi0)**

π₀ is a Vision-Language-Action model for general robot control, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.

**Model Overview**

π₀ represents a breakthrough in robotics as the first general-purpose robot foundation model developed by Physical Intelligence. Unlike traditional robots that are narrow specialists programmed for repetitive motions, π₀ is designed to be a generalist policy that can understand visual inputs, interpret natural language instructions, and control a variety of different robots across diverse tasks.

For more details, see the [Physical Intelligence π₀ blog post](https://www.physicalintelligence.company/blog/pi0).


This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).

---

## How to Get Started with the Model

For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:

### Train from scratch

```bash
lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --policy.type=act \
  --output_dir=outputs/train/<desired_policy_repo_id> \
  --job_name=lerobot_training \
  --policy.device=cuda \
  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_policy_repo_id>/checkpoints/.

Evaluate the policy/run inference

lerobot-record \
  --robot.type=so100_follower \
  --dataset.repo_id=<hf_user>/eval_<dataset> \
  --policy.path=<hf_user>/<desired_policy_repo_id> \
  --episodes=10

Prefix the dataset repo with eval_ and supply --policy.path pointing to a local or hub checkpoint.

---

Model Details

• License: apache-2.0