Title

Generating Muscle Driven Arm Movements Using Reinforcement Learning

Authors

Alex S. Broad, Washington University in St Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

2011

Filename

WUCSE-2011-20.pdf

DOI:

10.7936/K7RX9983

Technical Report Number

WUCSE-2011-20

Abstract

This project focuses on using Reinforcement Learning to optimize arm dynamics through muscle control for desired trajectories. The goal of this project was to create a tool that can be used to gain a better understanding of the arm’s muscles and collect information that is useful in many other disciplines such as biomechanics, anthropology, medicine and robotics. I developed biologically realistic models of primate arm's using Stanford’s SimTK software, an open-source tool for modeling musculoskeletal structures. I then made use of Differential Dynamic Programming in order to generate novel movements from first principles and optimize motion over a specified trajectory. Lastly, I demonstrated the usefulness of this tool by examining its effectiveness in discovering the consequences of different muscle groups on the optimal behavior policy.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7RX9983

Recommended Citation

Broad, Alex S., "Generating Muscle Driven Arm Movements Using Reinforcement Learning" Report Number: WUCSE-2011-20 (2011). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/56