Publication
Effects of a social force model reward in robot navigation based on deep reinforcement learning
Conference Article
Conference
Iberian Robotics Conference (ROBOT)
Edition
4th
Pages
213-224
Doc link
https://doi.org/10.1007/978-3-030-36150-1_18
File
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.
Categories
ART neural nets, learning (artificial intelligence), mobile robots, social aspects of automation.
Scientific reference
O. Gil and A. Sanfeliu. Effects of a social force model reward in robot navigation based on deep reinforcement learning, 4th Iberian Robotics Conference, 2019, Porto, Portugal, Vol 1093 of Advances in Intelligent Systems and Computing, pp. 213-224, Springer.
Follow us!