Bridging the Gap: Regularized Reinforcement Learning for Improved Classical Motion Planning with Safety Modules

Classical navigation planners can provide safe navigation, albeit often suboptimally and with hindered human norm compliance. ML-based, contemporary autonomous navigation algorithms can imitate more natural and humancompliant navigation, but usually require large and realistic datasets and do not always provide safety guarantees. We present an approach that leverages a classical algorithm to guide reinforcement learning. This greatly improves the results and convergence rate of the underlying RL algorithm and requires no human-expert demonstrations to jump-start the process. Additionally, we incorporate a practical fallback system that can switch back to a classical planner to ensure safety. The outcome is a sample efficient ML approach for mobile navigation that builds on classical algorithms, improves them to ensure human compliance, and guarantees safety.

PTDRL: Parameter Tuning using Deep Reinforcement Learning

A variety of autonomous navigation algorithms exist that allow robots to move around in a safe and fast manner. However, many of these algorithms require parameter re-tuning when facing new environments. In this paper, we propose PTDRL, a parameter-tuning strategy that adaptively selects from a fixed set of parameters those that maximize the expected reward for a given navigation system. Our learning strategy can be used for different environments, different platforms, and different user preferences. Specifically, we attend to the problem of social navigation in indoor spaces, using a classical motion planning algorithm as our navigation system and training its parameters to optimize its behavior. Experimental results show that PTDRL can outperform other online parameter-tuning strategies.