TTGO: Tensor Train for Global Optimization Problems in Robotics

A PyTorch implementation of TTGO algorithm and the applications presented in the paper "Tensor Train for Global Optimization Problems in Robotics "

Website: https://sites.google.com/view/ttgo/home

Paper: https://arxiv.org/pdf/2206.05077.pdf

Pre-requistes

• Install the tntorch library from: https://github.com/rballester/tntorch (pip install tntorch)
• Pybullet (only required for visualization of robotics applications): https://pypi.org/project/pybullet/
• RoMa (only required robotic applications; for quarternion calculations): https://naver.github.io/roma/

Overview

• ./ttgo.py: the TTGO algorithm is defined in this class
• ./function_optimization/: includes the application of ttgo for optimization of several benchmark functions
  • Recommendation: try these notebooks first to understand the approach
• ./toy_robots/: application of ttgo for simple toy models of robotics problems (planar manipulator IK and reaching tasks)
• ./manipulator/: application of ttgo for IK and reaching tasks with some standard manipulators

Note: All the implementations are fully compatible for use with GPU. For faster computation, it is highly recommended to use GPU

For any questions, contact the author Suhan Shetty suhan.shetty@idiap.ch