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End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance

Summary

Researchers developed a real-time end-to-end controller for powered knee-ankle prostheses using Temporal Convolutional Networks, eliminating need for manual tuning and intent classifiers. Tested on level ground, ramps, and stairs, the controller demonstrated seamless adaptation, matching training-data scaling and enabling natural stair transitions without subject-specific adjustments.

SourcearXiv RoboticsAuthor: John Shim, Christoph Nuesslein, Sixu Zhou, Hanjun kim, Kinsey Herrin, Aaron Young
End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance
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[2606.07902] End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance

[Submitted on 5 Jun 2026]

Title:End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance

View a PDF of the paper titled End-to-End Control of a Powered Knee-Ankle Prosthesis Towards Unified, Tuning-Free Assistance, by John Shim and 5 other authors

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Abstract:Powered prostheses conventionally rely on impedance controllers that require extensive manual tuning and explicit mode classification. In this work, we present real-time deployment of an end-to-end prosthesis controller that estimates continuous actuator signals from onboard sensors, eliminating the need for intent classifiers and subject-specific tuning. Temporal Convolutional Networks were trained on a multi-terrain dataset from 18 individuals with transfemoral amputation and deployed in real time across five locomotion modes. Four participants (three able-bodied, one with transfemoral amputation) ambulated across level ground, ramp ascent and descent, and stair ascent and descent. During level walking, the deployed controller reproduced the training-data scaling of peak ankle torque with walking speed (deployed 0.85 Nm/kg per m/s, p = 0.001; training 0.96 Nm/kg per m/s, 95% CI [0.42, 1.50], p = 0.002), after excluding one outlier traced to atypical prosthesis loading. During ramp ascent, the controller scaled knee pre-flexion with grade (deployed 2.92 deg/deg, p = 0.027; training 3.30 deg/deg, 95% CI [1.83, 4.77], p

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Key points

  • End-to-end prosthesis controller uses Temporal Convolutional Networks to estimate actuator signals directly from sensors.
  • Eliminates manual tuning and explicit mode classification required by conventional impedance controllers.
  • Tested on 4 participants across 5 locomotion modes, showing consistent torque and knee angle scaling with terrain.
  • Seamless stair transitions achieved even for sequences not present in training data.

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