# Differentiable Gaussian Process Motion Planning

## TL;DR

This paper introduces a differentiable extension to Gaussian Process Motion Planning (GPMP2) that enables end-to-end learning of algorithm parameters, improving planning performance through data-driven adaptation.

## Contribution

It presents a novel differentiable version of GPMP2 allowing automatic parameter tuning via learning from experience.

## Key findings

- Enhanced planning success rate with learned parameters
- Improved adaptability across diverse tasks
- Validated effectiveness through multiple experiments

## Abstract

Modern trajectory optimization based approaches to motion planning are fast, easy to implement, and effective on a wide range of robotics tasks. However, trajectory optimization algorithms have parameters that are typically set in advance (and rarely discussed in detail). Setting these parameters properly can have a significant impact on the practical performance of the algorithm, sometimes making the difference between finding a feasible plan or failing at the task entirely. We propose a method for leveraging past experience to learn how to automatically adapt the parameters of Gaussian Process Motion Planning (GPMP) algorithms. Specifically, we propose a differentiable extension to the GPMP2 algorithm, so that it can be trained end-to-end from data. We perform several experiments that validate our algorithm and illustrate the benefits of our proposed learning-based approach to motion planning.