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
- 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/
- ./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 [email protected]