  Neuromorphic computing mimics computational principles of the brain in
$\textit{silico}$ and motivates research into event-based vision and spiking
neural networks (SNNs). Event cameras (ECs) exclusively capture local intensity
changes and offer superior power consumption, response latencies, and dynamic
ranges. SNNs replicate biological neuronal dynamics and have demonstrated
potential as alternatives to conventional artificial neural networks (ANNs),
such as in reducing energy expenditure and inference time in visual
classification. Nevertheless, these novel paradigms remain scarcely explored
outside the domain of aerial robots.
  To investigate the utility of brain-inspired sensing and data processing, we
developed a neuromorphic approach to obstacle avoidance on a camera-equipped
manipulator. Our approach adapts high-level trajectory plans with reactive
maneuvers by processing emulated event data in a convolutional SNN, decoding
neural activations into avoidance motions, and adjusting plans using a dynamic
motion primitive. We conducted experiments with a Kinova Gen3 arm performing
simple reaching tasks that involve obstacles in sets of distinct task scenarios
and in comparison to a non-adaptive baseline.
  Our neuromorphic approach facilitated reliable avoidance of imminent
collisions in simulated and real-world experiments, where the baseline
consistently failed. Trajectory adaptations had low impacts on safety and
predictability criteria. Among the notable SNN properties were the correlation
of computations with the magnitude of perceived motions and a robustness to
different event emulation methods. Tests with a DAVIS346 EC showed similar
performance, validating our experimental event emulation. Our results motivate
incorporating SNN learning, utilizing neuromorphic processors, and further
exploring the potential of neuromorphic methods.
