# Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning

Source: [Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning](<https://www.sota2.com/research/paper/68fa697b1e209131cea2255b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:01:46.798Z

Authors: Visak Kumar

Published: 2021-03-15T04:01:20.000Z

## Abstract

In this work, we develop an automated method to generate 3D human walking motion in simulation which is comparable to real-world human motion. At the core, our work leverages the ability of deep reinforcement learning methods to learn high-dimensional motor skills while being robust to variations in the environment dynamics. Our approach iterates between policy learning and parameter identification to match the real-world bio-mechanical human data. We present a thorough evaluation of the kinematics, kinetics and ground reaction forces generated by our learned virtual human agent. We also show that the method generalizes well across human-subjects with different kinematic structure and gait-characteristics.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.08125>)

[PDF](<https://arxiv.org/pdf/2103.08125.pdf>)