![Representation of bilevel optimization procedure for the robotic prosthesis. Left panel: The robotic knee torque, τ , is generated from the impedance control parameter values, which are obtained through real-time tuning by the RL controller. Lower right panel: This panel illustrates features of knee and thigh kinematics throughout a single gait cycle. For knee kinematics, superscript numbers 1–4 denote the respective phases within the gait cycle (i.e., STF, STE, SWF, and SWE), each corresponding to a knee feature. For thigh kinematics, the minimum value of the thigh angle acts as the feature. Top right panel: At the end of each bilevel optimization iteration, the cost function derived from IRL, each in a quadratic form, is utilized in the design of the RL controller. The implementation of the two interleaving procedures involving the inverse RL and forward RL is summarized in Algorithm 1. The RL controller’s inputs include kinematic features from the corresponding phase [defined in (5) and (6)], and its outputs involve adjustments to the impedance settings [defined in (3)].](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ttw5tcvxt52gbygtxrgvschk/bafkreicouj2gktsqqrlrflv7z4kwavlcomzy2uso32cntiuhx6fj3s3hdu)
Representation of bilevel optimization procedure for the robotic prosthesis. Left panel: The robotic knee torque, τ , is generated from the impedance control parameter values, which are obtained through real-time tuning by the RL controller. Lower right panel: This panel illustrates features of knee and thigh kinematics throughout a single gait cycle. For knee kinematics, superscript numbers 1–4 denote the respective phases within the gait cycle (i.e., STF, STE, SWF, and SWE), each corresponding to a knee feature. For thigh kinematics, the minimum value of the thigh angle acts as the feature. Top right panel: At the end of each bilevel optimization iteration, the cost function derived from IRL, each in a quadratic form, is utilized in the design of the RL controller. The implementation of the two interleaving procedures involving the inverse RL and forward RL is summarized in Algorithm 1. The RL controller’s inputs include kinematic features from the corresponding phase [defined in (5) and (6)], and its outputs involve adjustments to the impedance settings [defined in (3)].
Authors introduce a method that personalizes robotic #ProstheticLeg control by optimizing both the #prosthesis and the user’s residual limb via #InverseReinforcementLearning. This enables more natural walking and improved long-term health for amputees
https://ieeexplore.ieee.org/document/11251175
![#prosthesis (pl.: prostheses; from Ancient Greek: πρόσθεσις, romanized: prósthesis, lit. 'addition, application, attachment'),[1] or a prosthetic implant,[2][3] is an artificial device that replaces a missing body part,](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:kwb4no4g6e3dkyysuzjoenh4/bafkreihk7qvkyt5wlt4u4ie3p5usd5ctimwrvts63mlp43ggpbtwn7n2eu)
