🧪PI‑0

1. What is PI0 and why KNX needs it

PI0 is a Large Behavior Model (LBM) for robotic manipulation. It takes a history of observations (camera images, joint states) and predicts low-level actions (e.g., torques or joint deltas). It’s trained on large, diverse demonstrations across multiple labs and robot platforms.

In the LeRobot ecosystem, PI0 is distributed as a ready-to-use policy:

• Base model on Hugging Face: lerobot/pi0_base

Why this matters for KNX:

• An open-source “brain” for manipulators you can run locally.

• A convenient baseline to compare against other VLA/LBM models.

• Useful for benchmarking KNX-related simulation scenes.

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2. GPU server prep (A10 on vast.ai or similar)

Recommended minimum:

• GPU: NVIDIA A10 (24 GB) or better

• CPU: 8+ vCPU

• RAM: 32 GB

• OS: Ubuntu 20.04 / 22.04

• NVIDIA drivers + CUDA 12.x installed (common on vast.ai images)

Assumptions:

• You have SSH access, e.g.:

ssh USERNAME@your-server-ip

• Working under /data.

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3. Miniconda and the lerobot environment

cd /data

# Install Miniconda
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh
bash miniconda.sh -b -p /data/miniconda

# Activate conda in this shell
eval "$(/data/miniconda/bin/conda shell.bash hook)"
conda init
exec $SHELL

# Create env
conda create -y -n lerobot python=3.10
conda activate lerobot

Set up Hugging Face cache:

export HF_HOME=/data/hf_home
mkdir -p "$HF_HOME"

(Add these to ~/.bashrc if you want them persistent.)

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4. System deps (MuJoCo/robosim, ffmpeg, OpenGL)

sudo apt update
sudo apt install -y \
  git \
  ffmpeg \
  libgl1-mesa-glx \
  libglib2.0-0 \
  libglfw3 \
  libosmesa6

MuJoCo is pulled via Python packages, but GL libraries are still needed.

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5. Install LeRobot from source

cd /data
git clone https://github.com/huggingface/lerobot.git
cd lerobot

conda activate lerobot
# LeRobot + policies (includes PI0)
pip install -e ".[policies]"

If hf-egl-probe build fails due to missing cmake:

pip install cmake
pip install hf-egl-probe

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6. Smoke test: run PI0 on Aloha (example)

Create eval_pi0_aloha_live.py under /data/lerobot:

# file: eval_pi0_aloha_live.py
import torch

from lerobot.envs.factory import make_env
from lerobot.policies.pi0.policy import PI0Policy


def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Example: Aloha insertion task (ensure gym_aloha is installed)
    env = make_env(
        env_type="gym_aloha",
        task_id="AlohaInsertion-v0",
        obs_type="image",
        render_mode="rgb_array",
    )

    policy = PI0Policy.from_pretrained("lerobot/pi0_base")
    policy.to(device)
    policy.eval()

    num_episodes = 5
    for ep in range(num_episodes):
        obs, info = env.reset()
        done = False
        step_idx = 0
        while not done:
            with torch.no_grad():
                action = policy.act(obs, device=device)
            obs, reward, terminated, truncated, info = env.step(action)
            done = bool(terminated or truncated)
            step_idx += 1
        print(f"Episode {ep} finished, steps={step_idx}, success={info.get('is_success', False)}")


if __name__ == "__main__":
    main()

Run:

cd /data/lerobot
conda activate lerobot
python eval_pi0_aloha_live.py

Expected:

• lerobot/pi0_base weights download from Hugging Face.

• Several episodes in simulation; success flag printed per episode.

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7. KNX demo script: run and record a video

Save pi0_run_and_record.py:

# file: pi0_run_and_record.py
import os
import cv2
import torch
import numpy as np

from lerobot.envs.factory import make_env
from lerobot.policies.pi0.policy import PI0Policy


def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    env = make_env(
        env_type="gym_aloha",
        task_id="AlohaInsertion-v0",
        obs_type="image",
        render_mode="rgb_array",
        fps=30,
    )

    policy = PI0Policy.from_pretrained("lerobot/pi0_base")
    policy.to(device)
    policy.eval()

    out_path = "pi0_aloha_episode.mp4"
    fps = 30
    frames = []

    obs, info = env.reset()
    done = False
    step_idx = 0

    while not done:
        with torch.no_grad():
            action = policy.act(obs, device=device)
        obs, reward, terminated, truncated, info = env.step(action)
        done = bool(terminated or truncated)
        frame = env.render()
        if frame is not None:
            frames.append(frame)
        step_idx += 1

    print(f"Episode finished, steps={step_idx}, success={info.get('is_success', False)}")

    if len(frames) == 0:
        print("No frames collected, check env.render()")
        return

    h, w, _ = frames[0].shape
    writer = cv2.VideoWriter(
        out_path,
        cv2.VideoWriter_fourcc(*"mp4v"),
        fps,
        (w, h),
    )
    for f in frames:
        bgr = cv2.cvtColor(f, cv2.COLOR_RGB2BGR)
        writer.write(bgr)
    writer.release()
    print(f"Saved video to {out_path}")


if __name__ == "__main__":
    main()

Run:

cd /data/lerobot
conda activate lerobot
python pi0_run_and_record.py

Use the resulting pi0_aloha_episode.mp4 in KNX docs or demos.

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8. Pros and cons for KNX

Pros:

• Fully open model and weights (lerobot/pi0_base).

• Runs comfortably on a single A10.

• Integrated with LeRobot (multi-dataset, multiple policies).

• Strong baseline across different sim scenes (robosuite, LIBERO, gym_aloha).

Cons:

• Not a text-conditioned VLA that follows arbitrary prompts; it’s a strong behavior policy for specific tasks.

• Without tailored integration to KNX scenes, looks more like a classic RL/IL baseline than a generalist KNX agent.

• Requires GPU and simulator setup, which is non-trivial for casual users.