Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning

TL;DR

This paper integrates the Social Force Model into a Deep Reinforcement Learning framework to enhance robot navigation by improving state representation and reward functions, leading to better obstacle handling and navigation performance.

Contribution

It introduces a novel reward function based on SFM and incorporates forces into the state description for improved robot navigation in Deep RL.

Findings

Enhanced navigation performance in simulations

Better obstacle representation with SFM inclusion

Improved handling of moving agents and obstacles

Abstract

In this paper is proposed an inclusion of the Social Force Model (SFM) into a concrete Deep Reinforcement Learning (RL) framework for robot navigation. These types of techniques have demonstrated to be useful to deal with different types of environments to achieve a goal. In Deep RL, a description of the world to describe the states and a reward adapted to the environment are crucial elements to get the desire behaviour and achieve a high performance. For this reason, this work adds a dense reward function based on SFM and uses the forces in the states like an additional description. Furthermore, obstacles are added to improve the behaviour of works that only consider moving agents. This SFM inclusion can offer a better description of the obstacles for the navigation. Several simulations have been done to check the effects of these modifications in the average performance.