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Published August 2020 | Accepted Version
Book Section - Chapter Open

Recurrent Neural Network Control of a Hybrid Dynamical Transfemoral Prosthesis with EdgeDRNN Accelerator

Abstract

Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience. This paper describes the first steps toward learning complex controllers for dynamical robotic assistive devices. We provide the first example of behavioral cloning to control a powered transfemoral prostheses using a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) running on a custom hardware accelerator that exploits temporal sparsity. The RNN is trained on data collected from the original prosthesis controller. The RNN inference is realized by a novel EdgeDRNN accelerator in real-time. Experimental results show that the RNN can replace the nominal PD controller to realize endto-end control of the AMPRO3 prosthetic leg walking on flat ground and unforeseen slopes with comparable tracking accuracy. EdgeDRNN computes the RNN about 240 times faster than real time, opening the possibility of running larger networks for more complex tasks in the future. Implementing an RNN on this real-time dynamical system with impacts sets the ground work to incorporate other learned elements of the human-prosthesis system into prosthesis control.

Additional Information

© 2020 IEEE. This work was supported by the Samsung Global Research Neuromorphic Processor Project, the National Science Foundation Graduate Research Fellowship under Grant No. DGE1745301 and NSF NRI Grant No. 1724464 and Swiss National Center of Competence in Research Robotics (NCCR Robotics). This research was approved by California Institute of Technology Institutional Review Board with protocol no. 16-0693 for human subject testing. The authors also gratefully acknowledge discussions with Andrew Taylor and Prof. Yisong Yue, and the opportunity to build the first prototype at the July 2019 Telluride Neuromorphic Engineering Workshop.

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Created:
August 19, 2023
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October 20, 2023