Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning

Michael Ehrlich, Yuval Zaidel, Patrice L. Weiss, Arie Melamed Yekel, Naomi Gefen, Lazar Supic, Elishai Ezra Tsur

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number1007736
Pages (from-to)1007736
JournalFrontiers in Neuroscience
StatePublished - 29 Sep 2022


  • Neural Engineering Framework (NEF)
  • clinical robotic study
  • neuromorphic control
  • neurorehabilitation
  • online learning
  • prescribed error sensitivity

All Science Journal Classification (ASJC) codes

  • General Neuroscience


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