MSTOMP

Multi-policy Stochastic Trajectory Optimization for Motion Planning

1. Introduction

This Stochastic Trajectory Optimization Method is explained in our paper: Stochastic Trajectory Optimization for Robotic Skill Acquisition From a Suboptimal Demonstration. In this code base, we present an example demo to show how to use MSTOMP in a Learning from Demonstration Task. We use both the Franka Panda and Unitree Z1 robotic arm in the Pybullet to show our method's performance.

2. Quick use

2.1 Environment Setup

pip install pybullet, numpy

You may occur error message : error: Microsoft Visual C++ 14.0 or greater is required. Get it with "Microsoft C++ Build Tools": https://visualstudio.microsoft.com/visual-cpp-build-tools/ [end of output], then go to the website mentioned above and download VStudio

2.2 Run the demo

python ./Pybullet/demo.py --expt-name='your expert name'