LePaniPuri

🍽️ From Street Food to Smart Robots

LePaniPuri was born at the Embodied AI Hackathon 2025, organized by Seeed Studio. Our goal was simple but ambitious - to teach robots the cultural art of making Pani Puri, one of India’s most beloved street snacks.

We wanted to explore how embodied intelligence could be applied to a task that is instinctive for humans but rich in perception, coordination, and timing for robots. The result was LePaniPuri - a bimanual robot system powered by Jetson Thor and Groot N1.5, capable of picking, filling, and serving puris with remarkable precision

🤖 Key Features

We used two pairs of SO-101 robotic arms, configured in a leader–follower arrangement.

• The leader arms capture human demonstrations and teleoperation data.
• The follower arms replicate the learned behavior in real-time.

This configuration allowed us to model coordinated bimanual manipulation - one arm holds and positions the puri while the other performs fine tasks like poking holes, stuffing fillings, or pouring flavored water.

⚙️ Jetson Thor Setup

Flash Jetson Thor with JetPack 7.0 (L4T 38.2) using instructions here.

After flashing JetPack 7.0, install the whole JetPack component software on the thor:

sudo apt update
sudo apt install nvidia-jetpack

Make sure the Docker daemon configuration file is as follow:

sudo apt install -y jq
sudo jq '. + {"default-runtime": "nvidia"}' /etc/docker/daemon.json | \
  sudo tee /etc/docker/daemon.json.tmp && \
  sudo mv /etc/docker/daemon.json.tmp /etc/docker/daemon.json

Make sure */etc/docker/daemon.json* file looks like following:

{
  "runtimes": {
      "nvidia": {
      "args": [],
      "path": "nvidia-container-runtime"
      }
  },
  "default-runtime": "nvidia"
}

Add your username to the docker group:

sudo usermod -aG docker $USER
newgrp docker

On Jetson Thor, Docker Compose v2 is to be installed which is distributed through Ubuntu 24.04's official apt repository:

sudo apt install docker-compose-v2

Getting Started

Clone the lepanipuri repository:

git clone git@github.com:robo-trail/lepanipuri.git -b pre-release
cd lepanipuri 

Fetch lepanipuri/thor-isaac-groot:v0.1 docker image from Docker Hub:

docker pull lepanipuri/thor-isaac-groot:v0.1

Since there are 4 SO101 arms, get Serial Short ID of each one:

udevadm info --query=property --name=/dev/tty{YOUR} | grep ID_SERIAL_SHORT

Once you have ID_SERIAL_SHORT of all the 4 arms, modify ./lerobot/setup_usb_ports.sh file your specific ones:

# Define the ID_SERIAL_SHORT values for each device
DEVICE1_ID_SERIAL_SHORT="5AAF270447" # left follower
DEVICE2_ID_SERIAL_SHORT="5AB0179027" # right follower
DEVICE3_ID_SERIAL_SHORT="5A7A015778" # left leader
DEVICE4_ID_SERIAL_SHORT="5AB0181062" # right leader

Now create the symbolic link so that even if the ports change on bootup, you can rely on symbolic link:

./lerobot/setup_usb_ports.sh

Use run.sh script to attach bash terminal within thor_docker container:

./docker/run.sh -t thor

Run the policy server inside one terminal of docker container:

./Isaac-GROOT/policy_server.sh

In another terminal inside docker container run policy client:

./Isaac-GROOT/policy_client.sh

### 🍛 The Task Flow

The full robot routine unfolds as:

1. **Pick and place puris** — grasp individual puris and position them on a plate.

2. **Poke a hole** — gently pierce the puri using a gripper-mounted pin.

3. **Stuff fillings** — insert a small potato ball inside the puri cavity.

4. **Pour flavored water** — squeeze the bottle to add *spicy or sweet* water, completing the snack.

Each step required precise visual feedback, stable force control, and synchronized arm coordination — all handled through Groot N1.5 and Thor’s onboard GPU.

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### 📊 Training and Datasets

We plan to release our training dataset and pipeline on **Hugging Face**, along with a **Brev-based training workflow** for retraining or fine-tuning models on new tasks.

This will allow the broader community to experiment with new recipes, manipulation techniques, and model architectures.

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### 🧩 Challenges We Faced

• Achieving **low-latency teleoperation** across x86 and ARM platforms
• Managing **USB serial consistency** across multiple SO-101 arms
• Synchronizing **policy server–client communication** in real-time
• Ensuring consistent torque calibration across arms for soft manipulation tasks

Each challenge helped refine our deployment strategy, making LePaniPuri a reproducible benchmark for future embodied AI research.

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🚀 What’s Next

We’re working on:

• Release Sphinx Documentation
• Release Thor Docker for LePaniPuri
• Release dataset on Hugging Face
• Release Brev training workflow---

🙌 Acknowledgements

This project was developed during the Embodied AI Hackathon 2025 by Seeed Studio, powered by NVIDIA Jetson Thor.

We’d like to thank:

• The SO-101 arm creators for their open-source design
• The Groot team for enabling embodied learning
• The Hugging Face LeRobot community for dataset standards
• And the broader open-source ecosystems for making robotics accessible