Because animals are extremely effective at moving in their natural environments they represent an excellent model to implement robust robotic movement and navigation. Braitenberg vehicles are bio- inspired models of animal navigation widely used in robotics. Tuning the parameters of these vehicles to generate appropriate behaviour can be challenging and time consuming. In this paper we present a Reinforce- ment Learning methodology to learn the sensori-motor connection of Braitenberg vehicle 3a, a biological model of source seeking. We present simulations of different stimuli and reward functions to illustrate the feasibility of this approach.
|Title of host publication||Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19--21, 2017, Proceedings|
|Publication status||E-pub ahead of print - 20 Jul 2017|
- Braitenberg vehicles
- Reinforcement learning
- Source seeking
Gillespie, J., Rano, I., Siddique, N., Santos, J., & Khamassi, M. (2017). Reinforcement Learning for Bio-Inspired Target Seeking. In Towards Autonomous Robotic Systems: 18th Annual Conference, TAROS 2017, Guildford, UK, July 19--21, 2017, Proceedings (pp. 637-650). Springer. https://doi.org/10.1007/978-3-319-64107-2_52