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Reinforcement Learning for Bio-Inspired Target Seeking

Abstract : 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 Reinforcement 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.
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Submitted on : Monday, January 14, 2019 - 11:40:47 AM
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James Gillespie, Iñaki Rañó, Nazmul Siddique, Jose Santos, Mehdi Khamassi. Reinforcement Learning for Bio-Inspired Target Seeking. Annual Conference Towards Autonomous Robotic Systems, Jul 2017, Guildford, United Kingdom. pp.637-650, ⟨10.1007/978-3-319-64107-2_52⟩. ⟨hal-01980224⟩



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