Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling

Understand

Learning long-horizon tasks such as navigation has presented difficult challenges for successfully applying reinforcement learning to robotics.

• From another perspective, under known environments, sampling-based planning can robustly find collision-free paths in environments without learning.
• In this work, we propose Control Transformer that models return-conditioned sequences from low-level policies guided by a sampling-based Probabilistic Roadmap (PRM) planner.
• We demonstrate that our framework can solve long-horizon navigation tasks using only local information.