Neuromorphic force-control in an industrial task: validating energy and latency benefits
arxiv(2024)
摘要
As robots become smarter and more ubiquitous, optimizing the power
consumption of intelligent compute becomes imperative towards ensuring the
sustainability of technological advancements. Neuromorphic computing hardware
makes use of biologically inspired neural architectures to achieve energy and
latency improvements compared to conventional von Neumann computing
architecture. Applying these benefits to robots has been demonstrated in
several works in the field of neurorobotics, typically on relatively simple
control tasks. Here, we introduce an example of neuromorphic computing applied
to the real-world industrial task of object insertion. We trained a spiking
neural network (SNN) to perform force-torque feedback control using a
reinforcement learning approach in simulation. We then ported the SNN to the
Intel neuromorphic research chip Loihi interfaced with a KUKA robotic arm. At
inference time we show latency competitive with current CPU/GPU architectures,
two orders of magnitude less energy usage in comparison to traditional
low-energy edge-hardware. We offer this example as a proof of concept
implementation of a neuromoprhic controller in real-world robotic setting,
highlighting the benefits of neuromorphic hardware for the development of
intelligent controllers for robots.
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