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Code Release for Learning Force Control for Legged Manipulation

Table of contents

  1. Overview
  2. System Requirements
  3. Training a Model
    1. Installation
    2. Environment and Model Configuration
    3. Training and Logging
    4. Analyzing the Policy

Overview

This repository provides an implementation of the paper:

Learning Force Control for Legged Manipulation
Tifanny Portela and Gabriel B. Margolis and Yandong Ji and Pulkit Agrawal
International Conference on Robotics and Automation, 2024
paper / project page

This environment builds on the legged gym environment by Nikita Rudin, Robotic Systems Lab, ETH Zurich (Paper: https://arxiv.org/abs/2109.11978) and the Isaac Gym simulator from NVIDIA (Paper: https://arxiv.org/abs/2108.10470). Training code builds on the rsl_rl repository, also by Nikita Rudin, Robotic Systems Lab, ETH Zurich. All redistributed code retains its original license.

Our initial release provides the following features:

  • Train a force control and locomotion policy for the Unitree B1 with Z1 arm.

System Requirements

Simulated Training and Evaluation: Isaac Gym requires an NVIDIA GPU. To train in the default configuration, we recommend a GPU with at least 10GB of VRAM. The code can run on a smaller GPU if you decrease the number of parallel environments (Cfg.env.num_envs). However, training will be slower with fewer environments.

Training a Model

Installation

Install pytorch 1.10 with cuda-11.3:

pip3 install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html

Install Isaac Gym

  1. Download and install Isaac Gym Preview 4 from https://developer.nvidia.com/isaac-gym

  2. unzip the file via:

    tar -xf IsaacGym_Preview_4_Package.tar.gz

  3. now install the python package

    cd isaacgym/python && pip install -e .

  4. Verify the installation by try running an example

    python examples/1080_balls_of_solitude.py

  5. For troubleshooting check docs isaacgym/docs/index.html

Install the b1_gym package

In this repository, run pip install -e .

Verifying the Installation

If everything is installed correctly, you should be able to run the test script with:

python scripts/test.py

You should see a GUI window with 10 B1+Z1 robots standing in place.

Environment and Model Configuration

CODE STRUCTURE The main environment for simulating a legged robot is in legged_robot.py. The default configuration parameters including reward weightings are defined in legged_robot_config.py::Cfg. Note that many are overridden in train.py.

There are three scripts in the scripts directory:

scripts
├── __init__.py
├── play.py
├── test.py
└── train.py

You can run the test.py script to verify your environment setup. If it runs then you have installed the gym environments correctly. To train an agent, run train.py. To evaluate a trained agent, run play.py.

Training and Logging

To train the compliant whole-body controller for B1+Z1, run:

python scripts/train.py

The script should print Saving model at iteration 0. It will log the training result to weights and biases. To visualize training progress, you can visit your weights and biases dashboard.

The GUI is off during training by default. To turn it on, set headless=False at the bottom of train.py i.e. train_b1_z1_IK(headless=False)

Training with the default configuration requires about 12GB of GPU memory. If you have less memory available, you can still train by reducing the number of parallel environments used in simulation (the default is Cfg.env.num_envs = 4000).

Analyzing the Policy

To evaluate a model, first edit the variable in play.py to point to its location in weights & biases:

RUN_PATH = [your_wandb_run]

e.g.

RUN_PATH = "robot-locomotion/b1-loco-z1-manip/runs/voq40aun"

Then run:

python scripts/play.py