Spiking Neural Networks (SNNs) coupled with neuromorphic hardware offer energy-efficient solutions for humanoid robot control. However, existing SNN-based motor control systems address bipedal locomotion and arm control in isolation, leaving integrated control of both unaddressed. We present a spiking architecture that coordinates force-based arm control and bipedal locomotion in a simulated humanoid, using the Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA). High-level action selection between locomotor and arm control is mediated by a biologically grounded spiking basal ganglia model. We validate the system through co-simulation of Nengo, for the neural control, and Isaac Sim, demonstrating successful target reaching, continuous digit drawing, path-following locomotion, and finally, switching between walking and arm control via basal ganglia disinhibition. To our knowledge, this is the first integrated spiking controller to combine bipedal locomotion and arm control on a full-scale humanoid platform. The full spike-based implementation enables future deployment on low-power neuromorphic hardware.

In this paper, we present a fully spiking neural network running on Intel’s Loihi chip for operational space control of a simulated 7-DOF arm. Our approach uniquely combines neural engineering and deep learning methods to successfully implement position and orientation control of the end effector. The development process involved four stages: (1) Designing a node-based network architecture implementing an analytical solution; (2) developing rate neuron networks to replace the nodes; (3) retraining the network to handle spiking neurons and temporal dynamics; and finally (4) adapting the network for the specific hardware constraints of the Loihi. We benchmark the controller on a center-out reaching task, using the deviation of the end effector from the ideal trajectory as our evaluation metric. The RMSE of the final neuromorphic controller running on Loihi is only slightly worse than the analytic solution, with 4.13% more deviation from the ideal trajectory, and uses two orders of magnitude less energy per inference than standard hardware solutions. While qualitative discrepancies remain, we find these results support both our approach and the potential of neuromorphic controllers. To the best of our knowledge, this work represents the most advanced neuromorphic implementation of neurorobotics developed to date.

Elishai Ezra Tsur, a multidisciplinary researcher, talks about the challenges that conventional academic mindset brought to his professional life. He, DeWolf, and Supic introduce us with their viewpoint about "data science" and its role in their research. In their recent work published in this issue of Patterns, they tackle the inverse kinematics problem using brain-inspired neuronal architectures.

Unmanned aerial vehicles (UAVs) need more autonomy. In light of inherent size, weight and power (SWaP) constraints, avionics with artificial intelligence implemented using neuromorphic technology offers a potential solution. We demonstrate intelligent drone control using spiking neural networks (SNNs), which can run on neuromorphic hardware. We present "BatSLAM", a modular SNN for autonomous localization, navigation, and control of UAVs. The bio-inspired algorithms are implemented using the neural modeling and simulation software Nengo, and are able to control the drone to autonomously perform a complex "house search" task in an AirSim simulated environment using only local sensor feedback. In this task, the drone is randomly placed and given a target object. The BatSLAM network localizes the drone, retrieves the target object location from memory, and then guides the drone along the most efficient path through the house to the target, avoiding obstacles and maneuvering through doors and stairways. We present benchmark results showing the BatSLAM network achieves 97.2% success rate in navigating to target objects in the house. To the best of our knowledge, the BatSLAM network presented here is the first in the world to carry out localization, navigation, and control in a fully spiking implementation.

Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well as recurrent, learning-based, and deep spiking neural networks for conventional and geometrically constrained inverse kinematics. We show that while highly parameterized data-driven neural networks with tens to hundreds of thousands of parameters exhibit sub-ms inference time and sub-mm accuracy, learning-based spiking architectures can provide reasonably good results with merely a few thousand neurons. Moreover, we show that spiking neural networks can perform well in geometrically constrained task space, even when configured to an energy-conserved spiking rate, demonstrating their robustness. Neural networks were evaluated on NVIDIA's Xavier and Intel's neuromorphic Loihi chip.

Brain-inspired neural network architecture overcomes unsolved classical control theory problem for telerobotics.

In this paper we demonstrate how the Nengo neural modeling and simulation libraries enable users to quickly develop robotic perception and action neural networks for simulation on neuromorphic hardware using tools they are already familiar with, such as Keras and Python. We identify four primary challenges in building robust, embedded neurorobotic systems, including: (1) developing infrastructure for interfacing with the environment and sensors; (2) processing task specific sensory signals; (3) generating robust, explainable control signals; and (4) compiling neural networks to run on target hardware. Nengo helps to address these challenges by: (1) providing the NengoInterfaces library, which defines a simple but powerful API for users to interact with simulations and hardware; (2) providing the NengoDL library, which lets users use the Keras and TensorFlow API to develop Nengo models; (3) implementing the Neural Engineering Framework, which provides white-box methods for implementing known functions and circuits; and (4) providing multiple backend libraries, such as NengoLoihi, that enable users to compile the same model to different hardware. We present two examples using Nengo to develop neural networks that run on CPUs and GPUs as well as Intel's neuromorphic chip, Loihi, to demonstrate two variations on this workflow. The first example is an implementation of an end-to-end spiking neural network in Nengo that controls a rover simulated in Mujoco. The network integrates a deep convolutional network that processes visual input from cameras mounted on the rover to track a target, and a control system implementing steering and drive functions in connection weights to guide the rover to the target. The second example uses Nengo as a smaller component in a system that has addressed some but not all of those challenges. Specifically it is used to augment a force-based operational space controller with neural adaptive control to improve performance during a reaching task using a real-world Kinova Jaco2 robotic arm. The code and implementation details are provided1, with the intent of enabling other researchers to build and run their own neurorobotic systems.

We present a spiking neuron model of the motor cortices and cerebellum of the motor control system. The model consists of anatomically organized spiking neurons encompassing premotor, primary motor, and cerebellar cortices. The model proposes novel neural computations within these areas to control a nonlinear three-link arm model that can adapt to unknown changes in arm dynamics and kinematic structure. We demonstrate the mathematical stability of both forms of adaptation, suggesting that this is a robust approach for common biological problems of changing body size (e.g. during growth), and unexpected dynamic perturbations (e.g. when moving through different media, such as water or mud). To demonstrate the plausibility of the proposed neural mechanisms, we show that the model accounts for data across 19 studies of the motor control system. These data include a mix of behavioural and neural spiking activity, across subjects performing adaptive and static tasks. Given this proposed characterization of the biological processes involved in motor control of the arm, we provide several experimentally testable predictions that distinguish our model from previous work.

Evaluating the effectiveness and performance of neuromorphic hardware is difficult. It is evenmore difficult when the task of interest is a closed-loop task; that is, a task where the outputfrom the neuromorphic hardware affects some environment, which then in turn affects thehardware’s future input. However, closed-loop situations are one of the primary potential uses ofneuromorphic hardware. To address this, we present a methodology for generating closed-loopbenchmarks that makes use of a hybrid of real physical embodiment and a type of minimalsimulation. Minimal simulation has been shown to lead to robust real-world performance, whilestill maintaining the practical advantages of simulation, such as making it easy for the samebenchmark to be used by many researchers. This method is flexible enough to allow researchersto explicitly modify the benchmarks to identify specific task domains where particular hardwareexcels. To demonstrate the method, we present a set of novel benchmarks that focus on motorcontrol for an arbitrary system with unknown external forces. Using these benchmarks, we showthat an error-driven learning rule can consistently improve motor control performance across arandomly generated family of closed-loop simulations, even when there are up to 15 interactingjoints to be controlled.

Neuroscience currently lacks a comprehensive theory of how cognitive processes can be implemented in a biological substrate. The Neural Engineering Framework (NEF) proposes one such theory, but has not yet gathered significant empirical support, partly due to the technical challenge of building and simulating large-scale models with the NEF. Nengo is a software tool that can be used to build and simulate large-scale models based on the NEF; currently, it is the primary resource for both teaching how the NEF is used, and for doing research that generates specific NEF models to explain experimental data. Nengo 1.4, which was implemented in Java, was used to create Spaun, the world's largest functional brain model (Eliasmith et al., 2012). Simulating Spaun highlighted limitations in Nengo 1.4's ability to support model construction with simple syntax, to simulate large models quickly, and to collect large amounts of data for subsequent analysis. This paper describes Nengo 2.0, which is implemented in Python and overcomes these limitations. It uses simple and extendable syntax, simulates a benchmark model on the scale of Spaun 50 times faster than Nengo 1.4, and has a flexible mechanism for collecting simulation results.

The ability to develop expertise through practice is a hallmark of biological systems, for both cognitive and motor based skills. At first, animals exhibit high variability and perform slowly, reliant on feedback signals constantly evaluating performance. With practice, the system develops a proficiency and consistency in skill execution, reflected in an increase in the associated cortical area (Pascual-Leone & Nguyet, 1995). Here we present a neural model of this expertise development. In the model, initial attempts at performing a task are based on generalizing previously learned control signals, which we refer to generically as ‘actions’, stored in the cortex. The basal ganglia evaluates these actions and modulates their contributions to the output signal, creating a novel action that performs the desired task. With repeated performance, the cortex learns to generate this action on its own, eventually developing an explicit representation of the action that can be called directly. This transference allows the system to more quickly and consistently execute the task, reflecting development of expertise. We present simulation results matching both behavioral and single cell spiking data.

A central challenge for cognitive and systems neuroscience is to relate the incredibly complex behavior of animals to the equally complex activity of their brains. Recently described, large-scale neural models have not bridged this gap between neural activity and biological function. In this work, we present a 2.5-million-neuron model of the brain (called "Spaun") that bridges this gap by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks.

Our empirical, neuroscientific understanding of biological motor systems has been rapidly growing in recent years. However, this understanding has not been systematically mapped to a quantitative characterization of motor control based in control theory. Here, we attempt to bridge this gap by describing the neural optimal control hierarchy (NOCH), which can serve as a foundation for biologically plausible models of neural motor control. The NOCH has been constructed by taking recent control theoretic models of motor control, analyzing the required processes, generating neurally plausible equivalent calculations and mapping them on to the neural structures that have been empirically identified to form the anatomical basis of motor control. We demonstrate the utility of the NOCH by constructing a simple model based on the identified principles and testing it in two ways. First, we perturb specific anatomical elements of the model and compare the resulting motor behavior with clinical data in which the corresponding area of the brain has been damaged. We show that damaging the assigned functions of the basal ganglia and cerebellum can cause the movement deficiencies seen in patients with Huntington's disease and cerebellar lesions. Second, we demonstrate that single spiking neuron data from our model's motor cortical areas explain major features of single-cell responses recorded from the same primate areas. We suggest that together these results show how NOCH-based models can be used to unify a broad range of data relevant to biological motor control in a quantitative, control theoretic framework.

This paper presents the dynamic scaling technique (DST), a method for control in large environments that dramatically reduces the resources required to achieve highly accurate movements. The DST uses a low resolution representation of the environment to calculate an initial approximately optimal trajectory and refines the control signal as the target is neared. Simulation results are presented and the effect of representation resolution on accuracy and computational efficiency is analyzed.