May 26, 2015

ICRA Tutorials: Bridging Gaps between Computational Biomechanics and Robotics

OpenSim Fellow Emel Demircan is organizing a tutorial at the International Conference on Robotics and Automation in Seattle, which will feature talks from several OpenSim researchers from Stanford and beyond.

Where: IEEE RAS ICRA in Seattle, WA
When: May 26, 2015

  • Emel Demircan, University of Tokyo & Stanford University
  • Yoshihiko Nakamura, University of Tokyo
  • Ajay Seth, Matt DeMers, Soha Pouya, Thomas Uchida, and Chris Dembia, Stanford University
  • Kat Steele, University of Washington
  • Emanuele Ruffaldi, Scuola Superiore Sant'Anna

Learn more on the tutorial website.

The impressive research advances in robotics and biomechanics have resulted in several robust, some freely available and open source control and simulation softwares for human and animal musculoskeletal systems. Simulation researchers who model humans and animals have created many software systems available commercially or in open source. Capabilities vary from purely kinematic simulations in graphics, to fully dynamic in biomechanics. Roboticists have also developed numerous controllers for robots and virtual humans and animals. Neural networks and machine learning have helped control redundant muscle-actuated models. Bio-inspired central pattern generators have controlled many robot types including salamanders and humanoids. This tutorial will strengthen the synergy between robotics and biomechanics for modeling the dynamics and simulating the motion of humans and animals. It aims at bridging gaps between robotics methods and dynamically consistent computational tools in biomechanics. Supporting a wide variety of research needs is a central objective of this tutorial. Among the elds that it is targeting are rehabilitation robotics, ergonomics, human performance, sports medicine, and orthopedic surgery. World experts will share their experience in a broad range of research, clinical, and commercial applications ranging from pre-operative planning for orthopedic surgery to understanding how assistive robotic devices interact with human musculoskeletal system.

Aug 07, 2018

Webinar: Robust control strategies for musculoskeletal models using deep reinforcement learning

An introduction to reinforcement learning and its application to developing control strategies more »

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