Pilot Behavior Cloning

(The official repo of ViDEN)

Visual Demonstration-based Embodiment-agnostic Navigation

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• Overview
• Getting Started
  • Dependencies
  • Installation
• Usage
  • Data
  • Train
  • Deploy
• Citing
• License

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Overview

This repository contains the code and resources related to an ongoing research of learning visual navigation policy in autonomous person-following task based on supervised Imitation Learning framework.

This framework involves collecting visual data from human demonstrations and labeling it with the odometry of the traversed trajectory. The resulting agent uses this visual context to generate action trajectories, employing generative models, including diffusion policies and transformers in a goal-condition fashion. The system enables a mobile robot to track a subject and to navigate around obstacles. This approach has the potential to simplifies and scale data collection and facilitates deployment in new environments (no need in mapping process) and robots (data is robot agnostic), by non-experts.

→ Collect data by hand in your environment

→ Clone behavior using Diffusion Policy

→ Deploy on your robot

This is research code, expect that it changes often and any fitness for a particular purpose is disclaimed.

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Getting Started

Dependencies

Project was tested on:

• ubuntu >=20
• python >= 3.8
• cuda >=11.7
• built on ROS noetic

Deploy:

• platforms: unitree go2, turtlebot3
• edge devices: Jetson orin dev-kit, Jetson orin nano

Additionally, the whole project-cycle (data collection till deploy) depends on following software:

• zion_ros_interface (interface wrapper for zed cameras and bag recording system)
• bagtool (bag processing and pilot_bc dataset structure creator)
• waypoints_follower_control (ROS wrapper for a simple waypoints reaching controller)

Installation

1. Clone repo:

git clone https://github.com/nimiCurtis/pilot_bc

2. Install the project:

cd pilot_bc && pip install -e .

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Usage

Data

In this project, data collection is performed by recording ROS bag files using a robot or manually with the appropriate system.

If you have a folder containing ROS bag files, you can process and extract them into a desired dataset structure by adjusting the relevant settings in the pilot_train/config/process_bag_config.yaml file. Once configured, you can run the following command to convert the bags to a dataset:

python pilot_train/bag2dataset.py

Dataset folder should be looked as follow:

├── <dataset folder name>
│   ├── <name_of_bag_batch1>
|   |    ├── raw_data
│   │       ├── raw_<topic1>.h5
│   │       ├── ...
│   │       └── raw_<topicN>.h5
|   |    ├── visual_data
|   |       ├── depth            
│   │           ├── 0.jpg
│   │           ├── ...
│   │           └── T.jpg
|   |       └── rgb          
│   │           ├── ...
│   │           └── T.jpg
│   │    ├── metadata.json
│   │    ├── robot_traj_data.json
│   │    ├── target_traj_data.json (when using the object detection topic)
│   │    └── traj_sample.mp4
│   ...
└── └── <name_of_bag_batchN>
         ├── raw_data
            ├── raw_<topic1>.h5
            ├── ...
            └── raw_<topicN>.h5
         ├── visual_data
             ├── depth            
                 ├── 0.jpg
                 ├── ...
                 └── T.jpg
             └── rgb          
                 ├── ...
                 └── T.jpg
         ├── metadata.json
         ├── robot_traj_data.json
         ├── target_traj_data.json (when using the object detection topic)
         └── traj_sample.mp4

Once the dataset is prepared,split and calculate stats by:

python pilot_train/data/data_split_and_stats.py -r <dataset folder name> -i <dataset folder path>

The stats will be saved in the relevant files located in the pilot_config directory, which will be used during the training phase.

Train

First, configure the main training configuration file at pilot_config/train_pilot_policy.yaml to match your training requirements (e.g., models, hyperparameters, etc.).

Note: Ensure a few absolute paths are updated accordingly.

Configuration files in this project are managed using the hydra lib, so it's recommended to familiarize yourself with hydra for efficient configuration management.

Once config is prepared, train a pilot agent with:

pyothon pilot_train/train.py

Note: All configs, logs, and model weights are saved in a folder within pilot_train/logs, with the folder name generated from the configuration file as follows:

... (some params)
log:
  ...
  run_name: train_pilot_policy/${policy_model.name}/${log.desc}${now:%Y-%m-%d}_${now:%H-%M-%S}
  ...

Deploy

The deployment stage is organized under the pilot_deploy directory.

First, create a checkpoints folder and a subfolder for your pilot agent:

cd pilot_deploy && mkdir checkpoints
cd checkpoints && mkdir <agent name>

Once you have a trained agent, copy (from the logs dir) its config.yaml file and model weights to pilot_deploy/checkpoints/<agent name>.

Now, all that remains is to deploy the agent by using the waypoints_follower_control ROS packages.

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Citing

The ViDEN data collection, training and deployment methods used in this software are described in the following paper (preprint). If you use this work in an academic context, please cite the following publication:

• N. Curtis, O. Azulay, and A. Sintov, "Embodiment-Agnostic Navigation Policy Trained with Visual Demonstrations", arXiv preprint (arXiv:2412.20226), 2024.

    @misc{curtis2024embodimentagnosticnavigationpolicytrained,
      title={Embodiment-Agnostic Navigation Policy Trained with Visual Demonstrations}, 
      author={Nimrod Curtis and Osher Azulay and Avishai Sintov},
      year={2024},
      eprint={2412.20226},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2412.20226} 
    }
  

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License

This repository is licensed under the Apache License. Feel free to use, modify, and distribute the code as per the terms of this license.

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