Control Transformer: Robot Navigation in Unknown Environments through PRM-Guided Return-Conditioned Sequence Modeling
This addresses the problem of applying reinforcement learning to robotics for navigation tasks, offering a method that works with local information and transfers to unknown settings, though it appears incremental by combining existing planning and learning techniques.
The authors tackled long-horizon robot navigation in unknown environments by proposing Control Transformer, which integrates a sampling-based planner with return-conditioned sequence modeling, achieving successful navigation and zero-shot sim2real transfer for various robots in mazes.
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. We evaluate our approach on partially-observed maze navigation with MuJoCo robots, including Ant, Point, and Humanoid. We show that Control Transformer can successfully navigate through mazes and transfer to unknown environments. Additionally, we apply our method to a differential drive robot (Turtlebot3) and show zero-shot sim2real transfer under noisy observations.