Stochastic Optimization for Trajectory Planning with Heteroscedastic Gaussian Processes
This work addresses motion planning challenges for robots in cluttered environments, representing an incremental improvement over existing Gaussian process methods.
The paper tackled the local minima problem in trajectory optimization for motion planning by proposing a stochastic optimization algorithm using the cross-entropy method with heteroscedastic Gaussian processes, resulting in a higher success rate in complex environments compared to GPMP2 while maintaining similar execution times.
Trajectory optimization methods for motion planning attempt to generate trajectories that minimize a suitable objective function. Such methods efficiently find solutions even for high degree-of-freedom robots. However, a globally optimal solution is often intractable in practice and state-of-the-art trajectory optimization methods are thus prone to local minima, especially in cluttered environments. In this paper, we propose a novel motion planning algorithm that employs stochastic optimization based on the cross-entropy method in order to tackle the local minima problem. We represent trajectories as samples from a continuous-time Gaussian process and introduce heteroscedasticity to generate powerful trajectory priors better suited for collision avoidance in motion planning problems. Our experimental evaluation shows that the proposed approach yields a more thorough exploration of the solution space and a higher success rate in complex environments than a current Gaussian process based state-of-the-art trajectory optimization method, namely GPMP2, while having comparable execution time.