A Replay-Constrained Simulation Framework for Personalization of Powered Knee--Ankle Prosthesis Controllers

• URL: http://arxiv.org/abs/2607.22858v1
• Date: Fri, 24 Jul 2026 18:59:48 GMT
• Title: A Replay-Constrained Simulation Framework for Personalization of Powered Knee--Ankle Prosthesis Controllers
• Abstract summary: Personalization of impedance controllers for powered prosthetic legs is critical to accommodating individual gait but remains challenging.<n>MuJoCo-based simulator reproduces prosthetic knee-ankle dynamics while replaying recorded hip kinematics and feedback-based ground reaction forces.<n>Deep reinforcement learning policy personalizes phase-dependent stiffness, damping, and equilibrium angle at both joints simultaneously.
• Score: 11.765906112645466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalization of impedance controllers for powered prosthetic legs is critical to accommodating individual gait biomechanics but remains challenging. Existing methods rely on time-intensive human-in-the-loop exploration and/or constrain optimization to low-dimensional, single-joint parameter subspaces. Sim-to-real transfer has enabled high-dimensional locomotion control for legged robots, but in assistive device control the human partner remains un-modelable. We present a replay-constrained simulation framework: a MuJoCo-based simulator reproduces prosthetic knee-ankle dynamics while replaying recorded hip kinematics and feedback-based ground reaction forces from individual walking data, bypassing the need to model complex human neuromuscular control mechanisms. We demonstrate the framework with a deep reinforcement learning policy that personalizes phase-dependent stiffness, damping, and equilibrium angle at both joints simultaneously, maximizing a biomimicry-based reward computed solely from onboard prosthesis measurements. Experiments with three participants with transfemoral amputation during level-ground walking at 0.8~m/s demonstrate strong simulation-to-hardware predictive validity (Pearson $r=0.96$--$0.997$). The best-performing policy on hardware was consistently predicted within the top five simulation policies for all participants. The learned controllers improved overall biomimicry rewards by 42--59\% relative to the unpersonalized baseline. The framework supports scalable high-dimensional personalization of powered prosthetic legs and is amenable to extension to higher-dimensional controller parameterizations such as neural-network controllers.

Related papers

• Pre-training Visual Dexterity in Simulation [67.17477115017896]
  We introduce a pre-training framework for dexterous manipulation that uses data entirely collected in simulation.<n>With the help of five operators, we collect 75 hours of multi-task dexterous manipulation over one week, and use it to pre-train a causal transformer on a sequence modeling objective.<n>We find that our approach outperforms training behavior cloning policies from scratch, showing that simulation teleoperation is a viable pre-training source for real-world dexterous manipulation.
  arXiv  Detail & Related papers  (2026-08-16T20:21:33Z)
• Beyond Heuristics: A Standardized Real2Sim Pipeline for Physical Human Robot Interaction in Human-in-the-Loop Simulation [7.749790302832782]
  We present a Real2Sim pipeline that identifies the coupled Physical Human-Robot Interaction (pHRI) dynamics of a pelvis-elastic-strap interface on an overground mobile balance assistant.<n>The pipeline thus improves HITL simulation fidelity and supports the Human Digital Twin as a predictive tool for pre-clinical verification of personalized controllers.
  arXiv  Detail & Related papers  (2026-07-03T06:52:37Z)
• Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks [53.12548325396181]
  Tendon drives paired with soft muscle actuation enable faster and safer robots.<n>We propose a sim-to-real pipeline that learns a neural network model of this complex actuation.<n>We successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation.
  arXiv  Detail & Related papers  (2026-04-10T16:52:54Z)
• Scaling Whole-Body Human Musculoskeletal Behavior Emulation for Specificity and Diversity [11.002363474183683]
  We introduce a large-scale parallel musculoskeletal computation framework for biomechanically grounded whole-body motion reproduction.<n>It achieves high joint angle accuracy and body position alignment for highly dynamic tasks such as dance, cartwheel, and backflip.<n>This work establishes a tractable computational route to analyzing the specificity and diversity underlying human embodied control of movement.
  arXiv  Detail & Related papers  (2026-03-31T06:55:10Z)
• Muscle Synergy Priors Enhance Biomechanical Fidelity in Predictive Musculoskeletal Locomotion Simulation [0.0]
  Human locomotion emerges from high-dimensional neuromuscular control, making predictive musculoskeletal simulation challenging.<n>We present a physiology-informed reinforcement-learning framework that constrains control using muscle synergies.<n>These results show that embedding neurophysiological structure into reinforcement learning can improve biomechanical fidelity and generalization in predictive human locomotion simulation with limited experimental data.
  arXiv  Detail & Related papers  (2026-03-11T06:48:15Z)
• Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation [0.0]
  We present a physics-based neuromusculoskeletal learning framework that trains a hip-exoskeleton control policy entirely in simulation.<n>A reinforcement learning teacher policy is trained using a muscle-synergy action prior over a wide range of walking speeds and slopes.<n>In simulation, exoskeleton assistance reduces mean muscle activation by up to 3.4% and mean positive joint power by up to 7.0% on level ground and ramp ascent.
  arXiv  Detail & Related papers  (2026-03-04T15:23:50Z)
• Dynamic Modeling and MPC for Locomotion of Tendon-Driven Soft Quadruped [0.0]
  SLOT (Soft Legged Omnidirectional Tetrapod) is a tendon-driven soft quadruped robot with 3D-printed TPU legs.<n>Each leg is modeled as a deformable continuum using discrete Cosserat rod theory.<n>A modular whole-body modeling framework is introduced, in which compliant leg dynamics are represented.
  arXiv  Detail & Related papers  (2026-02-18T11:14:22Z)
• CHIP: Adaptive Compliance for Humanoid Control through Hindsight Perturbation [70.5382178207975]
  hIsight Perturbation (CHIP) is a plug-and-play module that enables controllable end-effector stiffness.<n>CHIP is easy to implement and requires neither data augmentation nor additional reward tuning.<n>We show that a generalist motion-tracking controller trained with CHIP can perform a diverse set of forceful manipulation tasks.
  arXiv  Detail & Related papers  (2025-12-16T18:56:04Z)
• RobotDancing: Residual-Action Reinforcement Learning Enables Robust Long-Horizon Humanoid Motion Tracking [50.200035833530876]
  RobotDancing is a simple, scalable framework that predicts residual joint targets to explicitly correct dynamics discrepancies.<n>It can track multi-minute, high-energy behaviors (jumps, spins, cartwheels) and deploys zero-shot to hardware with high motion tracking quality.
  arXiv  Detail & Related papers  (2025-09-25T03:30:34Z)
• Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening [59.88594294676711]
  Modern deep learning based motion synthesis approaches barely consider the physical plausibility of synthesized motions. We propose a system Skeleton2Humanoid'' which performs physics-oriented motion correction at test time. Experiments on the challenging LaFAN1 dataset show our system can outperform prior methods significantly in terms of both physical plausibility and accuracy.
  arXiv  Detail & Related papers  (2022-10-09T16:15:34Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.