Bioinspired Smooth Neuromorphic Control for Robotic Arms
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Replicating natural human movements is a long-standing goal of robotics control theory. Drawing inspiration from biology, where reaching control networks give rise to smooth and precise movements, can narrow the performance gap between human and robot control. Neuromorphic processors, which mimic the brain's computational principles, are an ideal platform to approximate the accuracy and smoothness of such controllers while maximizing their energy efficiency and robustness. However, the incompatibility of conventional control methods with neuromorphic hardware limits the computational efficiency and explainability of their existing adaptations. In contrast, the neuronal connectome underlying smooth and accurate reaching movements is effective, minimal, and inherently compatible with neuromorphic processors. In this work, we emulate these networks and propose a biologically realistic spiking neural network for motor control. Our controller incorporates adaptive feedback to provide smooth and accurate motor control while inheriting the minimal complexity of its biological counterpart that controls reaching movements, allowing for direct deployment on Intel's neuromorphic processor. Using our controller as a building block and inspired by joint coordination in human arms, we scaled up our approach to control real-world robot arms. The trajectories and smooth, minimum-jerk velocity profiles of the resulting motions resembled those of humans, verifying the biological relevance of our controller. Notably, our method achieved state-of-the-art control performance while decreasing the motion jerk by 19% to improve motion smoothness. Our work suggests that exploiting both the computational units of the brain and their connectivity may lead to the design of effective, efficient, and explainable neuromorphic controllers, paving the way for neuromorphic solutions in fully autonomous systems.
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