Data-Driven Motion Planning for Robots

We systematically survey how data-driven approaches — deep neural networks, reinforcement learning, and large language models — can be applied to robot motion planning, and explore new directions in planning methods. By combining the strengths of classical search-based planning and learning-based planning, we aim at motion planning that achieves both adaptability and generalization in the real world.

Related papers:

• Gabriel Peixoto De Carvalho, Tetsuya Sawanobori, Takato Horii. “Data-Driven Motion Planning: A Survey on Deep Neural Networks, Reinforcement Learning, and Large Language Model Approaches”. IEEE Access, Vol. 13, 2025.