Neuromorphic Computing for Interactive Robotics: A Systematic Review
MUHAMMAD AITSAM, SERGIO DAVIES (Member, IEEE) AND ALESSANDRO DI NUOVO,
(Senior Member, IEEE)
Abstract: Modelling functionalities of the brain in human-robot interaction contexts requires a real-time understanding of how each part of a robot (motors, sensors, emotions, etc.) works and how they interact all together to accomplish complex behavioural tasks while interacting with the environment. Human brains are very efficient as they process the information using event-based impulses also known as spikes, which make living creatures very efficient and able to outperform current mainstream robotic systems in almost every task that requires real-time interaction. In recent years, combined efforts by neuroscientists, biologists, computer scientists and engineers make it possible to design biologically realistic hardware and models that can endow the robots with the required human-like processing capability based on neuromorphic computing and Spiking Neural Network (SNN). However, while some attempts have been made, a comprehensive combination of neuromorphic computing and robotics is still missing. In this article, we present a systematic review of neuromorphic computing applications for socially interactive robotics. We first introduce the basic principles, models and architectures of neuromorphic computation. The remaining articles are classified according to the applications they focus on. Finally, we identify the potential research topics for fully integrated socially interactive neuromorphic robots.
Introduction:
The biological intelligence of living beings has been an area of focus to explore their capabilities of memorising, thinking, perceiving, and acting accordingly. Among all species, humans have a remarkable capacity to make sound and quick decisions in diverse situations, sometimes based on vague and incomplete information. Humans perform complex behaviours that are important for surviving in dynamic environments. Advances in computational and behavioural neuroscience and embodied cognitive systems provide a baseline to integrate the interdisciplinary approaches for further technological progress in robotics. With increasing efforts of mimicking those functional and structural principles, roboticists have researched how the brain, robot sensors, and actuators operate together to perform complex tasks in a real-world environment [1]. To acquire more autonomy and operate in the real world, robots should: 1) perceive their environments in real-time, 2) process sparse information with energy efficiency and response latency, 3) behave under changing conditions and acquire self-learning ability.
With the emergence of increasingly powerful computers and sophisticated sensing systems, machine learning algorithms became increasingly capable and have achieved success in several scientific and commercial applications. Recently, advances have been made in deep-learning approaches based on the hierarchical nature of the human vision system [2]. However, the current dominant machine learning (ML) models in robots are far from performing human-like tasks that require precise motor control, fast reaction time and adaptation to external conditions. Besides this, these ML models also lack scalability. Furthermore, the divergence between the human brain and current technology can be exemplified by the fact that a hypothetical clock-based computer running a “human-scale” brain simulation requires approximately 12 Gigawatt of power. By contrast, the actual brain works with just 20 Watt [3]. A major bottleneck that severely limits the up-scaling of intelligent interactive agents is the unnatural discretization of time imposed by mainstream processing and sensing architectures [4], which are based on arbitrary internal clocks. Clock frequencies must be increased to deal with the continuous inputs of the real world. However, very high frequencies prove unfeasible and make large-scale applications of the current hardware inefficient.
To achieve such efficiency, living creatures process the information using spikes, which help them to perceive and act in the real world exceptionally well. A challenge for human-like machine intelligence is to imitate the efficient neuro-synaptic framework of the physical brain. This area of focus has been investigated extensively in recent years and many new technologies and methods are developed which try to mimic the biological behaviour of the human brain which consumes very less energy and acts very fast. One such method is Neuromorphic computing. Neuromorphic computing (also known as brain-inspired computing) is a multidisciplinary research paradigm that investigates large-scale processing systems that support natural neuronal computations through spike-driven communication. Compared to traditional approaches, key advantages of neuromorphic computing are energy efficiency, execution speed and robustness against local failures [5]. Currently, analog-programmable non-volatile memory (NVM) devices such as phase change memory (PCM) [6], resistive RAM (RRAM) [7], conductive bridging RAM (CBRAM) [8], magnetic RAM (STT-MRAM) [9] are the heart of these neuromorphic computing devices. The gradual switching of the resistance level in these devices are the key to neuromorphic computing and robotics applications [10]. Moreover, the neuromorphic design overcomes the distortion of the artificial discretization of time by using asynchronous event-driven computing that matches the temporal evolution of the external world [11]. Inspired by this event-driven type of information processing, emerging hardware and software knowledge in the field of neuroscience and electronics have made it possible to design biologically-inspired machines by using Spiking Neural Networks (SNNs) to model cognitive and interactive capabilities [12].
The interaction between humans and machines is of great relevance for both the field of neuromorphic computing and Robotics. Utilising neuromorphic technologies in robotics, from perception to motor control, is a promising approach to creating robots that can seamlessly integrate into society. In neurorobotics (neuromorphic computing and robotics), bio-inspired sensors are used to efficiently encode sensory signals. It also adapts to different environmental conditions by integrating inputs from multiple sensors and using event-based computation to accomplish desired tasks [13]. Figure 1 is summarising the landscape of neuromorphic computing and interactive robotics. Hardware and software simulators use specific neuron and synaptic models according to the desired applications.
For complete paper: Neuromorphic Computing for Interactive Robotics: A Systematic Review | IEEE Journals & Magazine | IEEE Xplore
