In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on uniform, stackable joint modules with three degrees of freedom each. Moreover, we present an approach for controlling these robots with recurrent spiking neural networks. At first, a spiking forward model learns motor-pose correlations from movement observations. After training, intentions can be projected back through unrolled spike trains of the forward model essentially routing the intention-driven motor gradients towards the respective joints, which unfolds goal-direction navigation. We demonstrate that spiking neural networks can thus effectively control trunk-like robotic arms with up to 75 articulated degrees of freedom with near millimeter accuracy.
University of Seville（塞维利亚大学）；Institute of Neuroinformatics University of Zurich and ETH Zurich（苏黎世大学与苏黎世联邦理工学院神经信息学研究所）
Biological nervous systems typically perform the control of numerous degrees of freedom for example in animal limbs. Neuromorphic engineers study these systems by emulating them in hardware for a deeper understanding and its possible application to solve complex problems in engineering and robotics. Central-Pattern-Generators (CPGs) are part of neuro-controllers, typically used at their last steps to produce rhythmic patterns for limbs movement. Different patterns and gaits typically compete through winner-take-all (WTA) circuits to produce the right movements. In this work we present a WTA ci…
A Spiking Neural Architecture for Coordinating Arm and Locomotor Control
Centre for Theoretical Neuroscience（理论神经科学中心）；Dept. of Systems Design Engineering, University of Waterloo（滑铁卢大学系统设计工程系）；Applied Brain Research（应用脑研究公司）；Dept. of Nanotechnology Engineering, University of Waterloo（滑铁卢大学纳米技术工程系）；Dept. of Philosophy, University of Waterloo（滑铁卢大学哲学系）
The Hong Kong Polytechnic University（香港理工大学）；Technical University of Denmark（丹麦技术大学）
While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepanci…
The University of New South Wales at the Australian Defence Force Academy（新南威尔士大学澳大利亚国防军学院分校）；DATA61 at CSIRO（澳大利亚联邦科学与工业研究组织DATA61部门）
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons. Most of the research employing spiking neurons has been non-behavioural and discontinuous. Comparatively, this paper presents a recurrent spiking controller that is capable of solving nonlinear control problems in continuous domains using a popular topology evolution algorithm as the learning mechanism. We propose two mechanisms necessary to the decoding of continuous signals from discrete spike transmission: (i) a b…
SpikingNav: Robust Embodied Navigation with Spiking Neural Policies
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；Beijing National Research Center for Information Science and Technology, Tsinghua University（北京信息科学与技术国家研究中心）
The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing …
Spiking Neural Networks for Continuous Control via End-to-End Model-Based Learning
Donders Centre for Cognition, Radboud University, Nijmegen, The Netherlands（鲁布·大学多纳中心，尼姆egen，荷兰）；Cajal Neuroscience Center, Spanish National Research Council, Madrid, Spain（西班牙国家科研 council Cajal 神经科学中心，马德里，西班牙）
The Hong Kong Polytechnic University（香港理工大学）；Technical University of Denmark（丹麦技术大学）
The cerebellum plays a distinctive role within our motor control system to achieve fine and coordinated motions. While cerebellar lesions do not lead to a complete loss of motor functions, both action and perception are severally impacted. Hence, it is assumed that the cerebellum uses an internal forward model to provide anticipatory signals by learning from the error in sensory states. In some studies, it was demonstrated that the learning process relies on the joint-space error. However, this may not exist. This work proposes a novel fully spiking neural system that relies on a forward predi…
Institute of Neuroinformatics, University of Zurich（苏黎世大学神经信息学研究所）；ETH Zurich（苏黎世联邦理工学院）；Istituto Italiano di Tecnologia（意大利技术研究院）
Despite neuromorphic engineering promises the deployment of low latency, adaptive and low power systems that can lead to the design of truly autonomous artificial agents, the development of a fully neuromorphic artificial agent is still missing. While neuromorphic sensing and perception, as well as decision-making systems, are now mature, the control and actuation part is lagging behind. In this paper, we present a closed-loop motor controller implemented on mixed-signal analog-digital neuromorphic hardware using a spiking neural network. The network performs a proportional control action by e…
Technical University of Munich（慕尼黑工业大学）；fortiss - Research Institute of the Free State of Bavaria（巴伐利亚自由州fortiss研究所）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；University of Waterloo（滑铁卢大学）
Central Pattern Generators (CPGs) models have been long used to investigate both the neural mechanisms that underlie animal locomotion as well as a tool for robotic research. In this work we propose a spiking CPG neural network and its implementation on neuromorphic hardware as a means to control a simulated lamprey model. To construct our CPG model, we employ the naturally emerging dynamical systems that arise through the use of recurrent neural populations in the Neural Engineering Framework (NEF). We define the mathematical formulation behind our model, which consists of a system of coupled…
Accenture Labs（埃森哲实验室）；National Research Council Canada（加拿大国家研究委员会）
In this paper, we explore the ability of a robot arm to learn the underlying operation space defined by the positions (x, y, z) that the arm's end-effector can reach, including disturbances, by deploying and thoroughly evaluating a Spiking Neural Network SNN-based adaptive control algorithm. While traditional control algorithms for robotics have limitations in both adapting to new and dynamic environments, we show that the robot arm can learn the operational space and complete tasks faster over time. We also demonstrate that the adaptive robot control algorithm based on SNNs enables a fast res…
