神经形态工程学
尖峰神经网络
计算机科学
人工智能
机器人
中心图形发生器
机器人学
人工神经网络
Spike(软件开发)
计算机视觉
机器学习
美学
软件工程
节奏
哲学
作者
Ashwin Sanjay Lele,Yan Fang,Justin Ting,Arijit Raychowdhury
出处
期刊:IEEE Transactions on Cognitive and Developmental Systems
[Institute of Electrical and Electronics Engineers]
日期:2022-09-01
卷期号:14 (3): 1092-1103
被引量:13
标识
DOI:10.1109/tcds.2021.3097675
摘要
Learning to adapt one's gait with environmental changes plays an essential role in the locomotion of legged robots which remains challenging for constrained computing resources and energy budget, as in the case of edge-robots. Recent advances in bio-inspired vision with dynamic vision sensors (DVSs) and associated neuromorphic processing can provide promising solutions for end-to-end sensing, cognition, and control tasks. However, such bio-mimetic closed-loop robotic systems based on event-based visual sensing and actuation in the form of spiking neural networks (SNNs) have not been well explored. In this work, we program the weights of a bio-mimetic multigait central pattern generator (CPG) and couple it with DVS-based visual data processing to show a spike-only closed-loop robotic system for a prey-tracking scenario. We first propose a supervised learning rule based on stochastic weight updates to produce a multigait producing spiking-CPG (SCPG) for hexapod robot locomotion. We then actuate the SCPG to seamlessly transition between the gaits for a nearest prey tracking task by incorporating SNN-based visual processing for input event-data generated by the DVS. This for the first time, demonstrates the natural coupling of event data flow from event-camera through SNN and neuromorphic locomotion. Thus, we exploit bio-mimetic dynamics and energy advantages of spike-based processing for autonomous edge-robotics.
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