Effects of a Social Force Model reward in Robot Navigation based on Deep Reinforcement Learning
This work addresses navigation challenges for robots in dynamic environments, but it is incremental as it builds on existing methods.
The paper tackles robot navigation by incorporating a Social Force Model (SFM) reward into a Deep Reinforcement Learning framework, resulting in improved performance in simulations with obstacles.
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.