Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

Subretinal injection is a delicate and complex microsurgery. The main surgical difficulties come from the surgeon's hand tremor, dexterous motion, and insufficient visual feedback. In order to begin addressing these challenges, this article presents a robot system for subretinal insertion integrated with intraoperative optical coherence tomography (OCT). The surgical workflow using this system consists of two main parts. The first part is the manual robot control, which aims the target before approaching the retinal surface, while considering the remote center of motion (RCM) constraint. When the injection area has been located precisely, needle is inserted into retina. To ensure surgical safety, needle insertion depth is estimated using OCT images on a continuous basis. A soft RCM control method is designed and integrated for the controller of our hybrid parallel-serial surgical robot. Safety and accuracy performance evaluation with a 15-ms control loop shows that the worst-case RCM deviation error is within 1 mm. Experimental results demonstrated that the proposed system has the ability to improve surgical outcomes by surgeons overcoming their physical limitations in order to enable a better dexterous motion, and furthermore enhancing their visual feedback for a better intraocular perception.

Pedestrian detection has attracted enormous research attention in the field of Intelligent Transportation System (ITS) due to that pedestrians are the most vulnerable traffic participants. So far, almost all pedestrian detection solutions are based on the conventional frame-based camera. However, they cannot perform very well in scenarios with bad light condition and high-speed motion. In this work, a Dynamic and Active Pixel Sensor (DAVIS), whose two channels concurrently output conventional gray-scale frames and asynchronous lowlatency temporal contrast events of light intensity, was first used to detect pedestrians in a traffic monitoring scenario. Data from two camera channels were fed into Convolutional Neural Networks (CNNs) including three YOLOv3 models and three YOLO-tiny models to gather bounding boxes of pedestrians with respective confidence map. Furthermore, a confidence map fusion method combining the CNN-based detection results from both DAVIS channels was proposed to obtain higher accuracy. The experiments were conducted on a custom dataset collected on TUM campus. Benefiting from the high speed, low latency and wide dynamic range of the event channel, our method achieved higher frame rate and lower latency than those only using a conventional camera. Additionally, it reached higher average precision by using the fusion approach.

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Building spiking neural networks (SNNs) based on biological synaptic plasticities holds a promising potential for accomplishing fast and energy-efficient computing, which is beneficial to mobile robotic applications. However, the implementations of SNNs in robotic fields are limited due to the lack of practical training methods. In this paper, we therefore introduce both indirect and direct end-to-end training methods of SNNs for a lane-keeping vehicle. First, we adopt a policy learned using the Deep Q-Learning (DQN) algorithm and then subsequently transfer it to an SNN using supervised learning. Second, we adopt the reward-modulated spike-timing-dependent plasticity (R-STDP) for training SNNs directly, since it combines the advantages of both reinforcement learning and the well-known spike-timing-dependent plasticity (STDP). We examine the proposed approaches in three scenarios in which a robot is controlled to keep within lane markings by using an event-based neuromorphic vision sensor. We further demonstrate the advantages of the R-STDP approach in terms of the lateral localization accuracy and training time steps by comparing them with other three algorithms presented in this paper.

Subretinal injection is a delicate and complex microsurgery, which requires surgeons to inject the therapeutic substance in a pre-operatively defined and intra-operatively updated subretinal target area. Due to the lack of subretinal visual feedback, it is hard to sense the insertion depth during the procedure, thus affecting the results of surgical outcome and hindering the widespread use of this treatment. This paper presents a novel approach to estimate the 3D position of the needle under the retina using the information from microscope-integrated Intraoperative Optical Coherence Tomography (iOCT). We evaluated our approach on both tissue phantom and ex-vivo porcine eyes. Evaluation results show that the average error in distance measurement is 4.7 μm (maximum of 16.5 μm). We furthermore, verified the feasibility of the proposed method to track the insertion depth of needle in robotassisted subretinal injection.

Snake-like robots with 3D locomotion ability have significant advantages of adaptive travelling in diverse complex terrain over traditional legged or wheeled mobile robots. Despite numerous developed gaits, these snake-like robots suffer from unsmooth gait transitions by changing the locomotion speed, direction, and body shape, which would potentially cause undesired movement and abnormal torque. Hence, there exists a knowledge gap for snake-like robots to achieve autonomous locomotion. To address this problem, this paper presents the smooth slithering gait transition control based on a lightweight central pattern generator (CPG) model for snake-like robots. First, based on the convergence behavior of the gradient system, a lightweight CPG model with fast computing time was designed and compared with other widely adopted CPG models. Then, by reshaping the body into a more stable geometry, the slithering gait was modified, and studied based on the proposed CPG model, including the gait transition of locomotion speed, moving direction, and body shape. In contrast to sinusoid-based method, extensive simulations and prototype experiments finally demonstrated that smooth slithering gait transition can be effectively achieved using the proposed CPG-based control method without generating undesired locomotion and abnormal torque.

This paper introduces an end-to-end learning approach based on Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) for a multi-layered spiking neural network (SNN). As a case study, a snake-like robot is used as an agent to perform target tracking tasks on the basis of our proposed approach. Since the key of R-STDP is to use rewards to modulate synapse strengthens, we first propose a general way to propagate the reward back through a multi-layered SNN. Upon the proposed approach, we build up an SNN controller that drives a snake-like robot for performing target tracking tasks. We demonstrate the practicability and advantage of our approach in terms of lateral tracking accuracy by comparing it to other state-of-the-art learning algorithms for SNNs based on R-STDP.