Neuro-Morphic Hardware for Real-Time Embodied Cognition in Humanoid Robots

Abstract

Embodying intelligence in humanoid robots requires continuous, low-latency integration of multimodal streams of sensory input along with adaptive control policies. Typical von Neumann architecture is ineffective at meeting hard power, latency, and parallelism constraints imposed by on-board, real-time embodied intelligence. This work outlines an integrated research study of neuromorphic hardware and spiking neural network architecture for providing real-time embodied cognition over a humanoid robotic platform. We architected a neuromorphic processing unit (NPU) based on a spiking architecture and co-designed sensor interfaces, learning rules, and motor control policies in order to capitalize upon event-driven computing. The system was evaluated in a series of closed-loop tasks centered upon real-time perception–action loops: reactive balance recovery, tactile-guided object manipulation, and fast visuomotor reaching under environmental perturbation. Performance was compared against a benchmark conventional controller running optimized deep learning and control stacks in low-power CPU/GPU hardware. We discover that the neuromorphic system facilitates equivalent task performance, decreased end-to-end perception-to-actuation latency, and decreased energy consumption by wide margins. More, the spiking networks enable continuous on-line adaptation based on local plasticity rules, enabling fast recovery from novel perturbations without off-line retraining. These outcomes indicate a direction for a potential use of neuromorphic hardware, when co-designed alongside embodied control algorithms, as providing a direction for energy-efficient, low-latency cognitive behavior in humanoid robots.