Neuromodulated Patience for Robot and Self-Driving Vehicle Navigation

TL;DR

This paper introduces a neuromodulation-inspired approach to robot navigation, where adjusting patience levels influences search behavior and route selection, enhancing autonomous navigation in dynamic and GPS-compromised environments.

Contribution

It applies serotonergic system principles to robot navigation, demonstrating how patience modulation affects search and route planning in outdoor experiments.

Findings

Adjusting patience levels changes search duration for target locations.

Deep reinforcement learning enables sidewalk following in GPS-challenged environments.

Patience modulation improves navigation efficiency under time constraints.

Abstract

Robots and self-driving vehicles face a number of challenges when navigating through real environments. Successful navigation in dynamic environments requires prioritizing subtasks and monitoring resources. Animals are under similar constraints. It has been shown that the neuromodulator serotonin regulates impulsiveness and patience in animals. In the present paper, we take inspiration from the serotonergic system and apply it to the task of robot navigation. In a set of outdoor experiments, we show how changing the level of patience can affect the amount of time the robot will spend searching for a desired location. To navigate GPS compromised environments, we introduce a deep reinforcement learning paradigm in which the robot learns to follow sidewalks. This may further regulate a tradeoff between a smooth long route and a rough shorter route. Using patience as a parameter may be beneficial for autonomous systems under time pressure.