Collision avoidance is a key technology enabling applications such as autonomous vehicles and robots. Various reinforcement learning techniques such as the popular Q-learning algorithms have emerged as a promising solution for collision avoidance in robotics. While spiking neural networks (SNNs), the third generation model of neural networks, have gained increased interest due to their closer resemblance to biological neural circuits in the brain, the application of SNNs to mobile robot navigation has not been well studied. Under the context of reinforcement learning, this paper aims to investigate the potential of biologically-motivated spiking neural networks for goal-directed collision avoidance in reasonably complex environments. Unlike the existing additive reward-modulated spike timing dependent plasticity learning rule (A-RM-STDP), for the first time, we explore a new multiplicative RM-STDP scheme
(M-RM-STDP) for the targeted application. Furthermore, we propose a more biologically plausible feed-forward spiking neural network architecture with fine-grained global rewards. Finally, by combining the above two techniques we demonstrate a further improved solution to collision avoidance. Our proposed approaches not only completely outperform Q-learning for cases where Q-learning can hardly reach the target without collision, but also significantly outperform a baseline SNN with A-RMSTDP in terms of both success rate and the quality of navigation trajectories.
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Navigating mobile robots to target in near shortest time using reinforcement learning with spiking neural networks
The autonomous navigation of mobile robots in unknown environments is of great interest in mobile robotics.
This article discusses a new strategy to navigate to a known target location in an unknown environment using a combination of the “go-to-goal” approach and reinforcement learning with biologically realistic spiking neural networks. While the “goto-goal” approach itself might lead to a solution for most environments, the added neural reinforcement learning in this work results in a strategy that takes the robot from a starting position to a target location in a near shortest possible time. To achieve the goal, we propose a reinforcement learning approach based on spiking neural networks. The presented biologically motivated delayed reward mechanism using eligibility traces results in a greedy approach that leads the robot to the target in a close to shortest possible time.
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- Award ID(s):
- 1639995
- NSF-PAR ID:
- 10026440
- Date Published:
- Journal Name:
- Proceedings of ... International Joint Conference on Neural Networks
- ISSN:
- 2161-4393
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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