A Tube-Based Reinforcement Learning Approach for Optimal Motion Planning in Unknown Workspaces

Panagiotis Rousseas; Charalampos P. Bechlioulis; Kostas J. Kyriakopoulos

ICRA 2024poster0 citations

A Tube-Based Reinforcement Learning Approach for Optimal Motion Planning in Unknown Workspaces

Panagiotis Rousseas, Charalampos P. Bechlioulis, Kostas J. Kyriakopoulos

Abstract

In this work, a tube-based nearly optimal solution to motion planning in unknown workspaces is presented. The advantages of reactive motion planning are combined with a Policy Iteration Reinforcement Learning scheme to yield a novel solution for unknown workspaces that inherits provable safety, convergence and optimality. Moreover, in simply-connected workspaces, our method is proven to asymptotically provide the globally optimal path. Our method is compared against a provably asymptotically optimal RRT⋆ method, as well as a relevant reactive method and provides satisfactory performance, closely matching or outperforming the former.

BibTeX

@inproceedings{icra2024_atubebasedreinfo,
  title = {A Tube-Based Reinforcement Learning Approach for Optimal Motion Planning in Unknown Workspaces},
  author = {Panagiotis Rousseas and Charalampos P. Bechlioulis and Kostas J. Kyriakopoulos},
  booktitle = {ICRA 2024},
  year = {2024}
}