师父 (shifu)

Shifu is a lightweight IsaacGym wrapper for robot learning. It trains robots in a massively parallel way like IsaacGym, but also it is simple to build, highly readable, and easily customized. This project aim to help robot learning researchers enjoy the incredible IsaacGym’s few minutes policy training without complex engineering.

Maintainer: Jaylon Wang

Affiliation: SenseTime

Contact: jwang383@ucsc.edu

中文-快速入门

Installation

We highly recommend creating a conda virtual environment to simplify this process

Prerequisites for Isaac Gym:

• Ubuntu 18.04, or 20.04.
• Python 3.6, 3.7, or 3.8
• NVIDIA driver version: 470.74 (or above)

1. Install requirements:

   • pip install -r requirements.txt -f https://download.pytorch.org/whl/cu113/torch_stable.html
     

2. Install Isaac Gym:

   • Download IsaacGym Preview 3, and follow the instructions in the documentation.
   • Run joint_monkey.py for install validation.
     • Note: If there is black window when running, add export VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/nvidia_icd.json to your ~/.bashrc, then run source ~/.bashrc may fix it.

3. Install rsl_rl:

   • Clone https://github.com/leggedrobotics/rsl_rl
   • cd rsl_rl && pip install -e .

4. Install shifu

   • Clone this repository
   • cd shifu && pip install -e .

Quickstart Guide

Example 1: Quadruped Robot with conditional commands on complex terrain

• Task: Control a legged robot A1 walk on complex terrains with various commands like base linear velocity, angular velocity, etc.
• Algorithm: PPO policy network
• Training time: 47.97 minutes

Train:

python -m examples.a1_conditional.a1_conditional -r train

play:

python -m examples.a1_conditional.a1_control_panel

Example 2: Arm Robot with multimodal vision

• Task: Push a cube to the target position using the end effector of an ABB robot by visual observation
• Algorithm: CNN regressor + PPO policy network
• Training time: 23 minutes (prior info input), 420 minutes (image input)

Train:

python -m examples.abb_pushbox_vision.a_prior_stage -r train
python -m examples.abb_pushbox_vision.b_regression_stage -r train
python -m examples.abb_pushbox_vision.c_vision_stage -r train

Play

python -m examples.abb_pushbox_vision.a_prior_stage -r play
python -m examples.abb_pushbox_vision.b_regression_stage -r play
python -m examples.abb_pushbox_vision.c_vision_stage -r play

API

ShifuVecGym

ShifuVecEnv is an object-orientated IsaacGym wrapper. The Pytorch tensors in the IsaacGym are directly managed by the Units(robot, objects, and sensors ) objects.

The style is similar to a gym environment:

• __init__: instantiate units and construct a IsaacGym simulation.
• compute_observations: collect all observation or state for the policy network.
• compute_termination: check the termination or done condition.
• build_reward_functions: build reward functions.(each term will be log to tensorboard automatically)

structure:

import torch
from shifu.gym import ShifuVecEnv

class MyShifuVecEnv(ShifuVecEnv):
    def __init__(self, cfg):
        super(MyShifuVecEnv, self).__init__(cfg)
        self.robot = Robot(RobotConfig())
        self.isg_env.create_envs(robot=self.robot)

    def compute_observations(self):
        self.obs_buf = torch.cat([
            self.robot.base_pose,
            self.actions
        ])

    def compute_termination(self):
        self.fail_buf = self.robot.base_pose[:, 2] < 0.
        self.time_out_buf = self.episode_length_buf > self.max_episode_length
        self.reset_buf = self.time_out_buf | self.fail_buf

    def build_reward_functions(self):
        return [self._success_reward]

    def _success_reward(self):
        return torch.square(self.robot.base_pose[:, 0])

PolicyRunner

After implementing a ShifuVecEnv, it can be trained by calling the run_policy function with proper config files.

from shifu.runner import run_policy

run_policy(
        run_mode="train",  # ['train', 'play', 'random']
        env_class=MyShifuVecEnv,
        env_cfg=MyEnvConfig(),
        policy_cfg=MyPPOConfig(),
        log_root="./logs/MyExperiment",
    )

Citing

The training process of example 1 (A1 conditional walking) is simplified version of legged gym environment by Nikita Rudin, Robotic Systems Lab, ETH Zurich.

The built-in PPO policy module is using rsl_rl by Nikita Rudin, Robotic Systems Lab, ETH Zurich.

Training simulation system is IsaacGymEnvs by Nvidia.