RIDER: Reinforcement-Based Inferred Dynamics via Emulating Rehearsals for Robot Navigation in Unstructured Environments

Autonomous navigation in unstructured environments is a challenging task due to the complex and dynamic nature of robot-terrain interactions. Existing approaches often struggle to generalize amidst the complexities of real-world settings. They tend to rely on hand-engineered, rule-based robot models or static weightings assigned to obstacles, semantics, and other perceptual cues to estimate traversability. To address these challenges, we propose a novel approach called Reinforcement-Based Inferred Dynamics via Emulating Rehearsals (RIDER), that learns the dynamics of robot-terrain interactions within a compact latent space, capturing robot’s traversability. Operating within a reinforcement learning paradigm, RIDER learns to infer its own dynamics by predicting how future robot observations and states evolve within this latent space in response to navigational behaviors. Furthermore, our approach leverages emulated rehearsals, where the robot learns within the latent space to predict its rewards and generate navigational behaviors, even when real observations have not been updated. Accordingly, RIDER equips robots with the ability to generate navigational behaviors by predicting environmental changes, and plan beyond the speed at which observations from sensors are available. Experimental results and comparisons with baseline methods establish that our proposed method outperforms other approaches in cluttered and unstructured environments and demonstrates an enhanced capacity for autonomous navigation in real-world settings.