Wheelchair-mounted robotic arms support people with upper extremity disabilities with various activities of daily living (ADL). However, the associated cost and the power consumption of responsive and adaptive assistive robotic arms contribute to the fact that such systems are in limited use. Neuromorphic spiking neural networks can be used for a real-time machine learning-driven control of robots, providing an energy efficient framework for adaptive control. In this work, we demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel’s Loihi chip. Our algorithm design uses neuromorphically represented and integrated velocity readings to derive the arm’s current state. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real-time. We pilot-tested the device with an able-bodied participant to evaluate its accuracy while performing ADL-related trajectories. We further demonstrated the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Videotaped recordings of ADL tasks performed by the robot were viewed by caregivers; data summarizing their feedback on the user experience and the potential benefit of the system is reported.

In the past few decades, bioinspired hexapod walking robots have attracted increasing attention, mainly due to their potential to efficiently traverse rough terrains. Recently, neuromorphic (brain-inspired) robotic control has been shown to outperform conventional control paradigms in stochastic environments. In this work, we propose a neuromorphic adaptive body leveling algorithm for a hexapod walking robot during transversal over multi-leveled terrain. We demonstrate adaptive control with distributed accelerator-driven neuro-integrators with only a few thousand spiking neurons. We further propose a framework for the integration of MuJoCo, a modeling environment, and Nengo, a spiking neural networks compiler, for efficient evaluation of neuromorphic control over high degrees of freedom robotic systems in realistic physics-driven scenarios.