OASIS: Optimization-Aware Subgoal Inference with Safety Constraints for Autonomous Mobile Robot Navigation
Overview
This research introduces OASIS, a hierarchical navigation framework that combines reinforcement learning subgoal generation with model predictive control. The system uses a Soft Actor-Critic policy to generate local subgoals while a nonlinear model predictive controller tracks them under motion limits and collision-avoidance constraints. Optimization outcomes from each subgoal interval are fed back to the policy without additional optimization calls. Evaluated across five navigation scenarios involving static and moving obstacles, OASIS achieves the highest aggregate navigation success compared to hierarchical and direct-goal control variants. Consistent advantages are observed across three training seeds when comparing matched configurations with and without optimization-status input.
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- arXiv Robotics — research abstractsSignalOASIS: Optimization-Aware Subgoal Inference with Safety Constraints for Autonomous Mobile Robot Navigation
This research introduces OASIS, a hierarchical navigation framework that combines reinforcement learning subgoal generation with model predictive control. The system uses a Soft Actor-Critic policy to generate local subgoals while a nonlinear model predictive controller tracks them under motion limits and collision-avoidance constraints. Optimization outcomes from each subgoal interval are fed back to the policy without additional optimization calls. Evaluated across five navigation scenarios involving static and moving obstacles, OASIS achieves the highest aggregate navigation success compared to hierarchical and direct-goal control variants. Consistent advantages are observed across three training seeds when comparing matched configurations with and without optimization-status input.
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