IEEE Robotics and Automation Society IEEE

Contact

Co-chairs:

katja mombaur
Katja Mombaur
Heidelberg University, Germany
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chris atkeson
Christopher G. Atkeson
Robotics Institute, CMU, USA
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thomas buschmann
Thomas Buschmann
TU Munich, Germany
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kensuke harada
Kensuke Harada
AIST Tsukuba, Japan
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abderrahmane kheddar
Abderrahmane Kheddar
CNRS-AIST Joint Robotics Laboratory, Japan
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Website:

www.tc-opt.uni-hd.de

Founding date:

Oct 11, 2012

Member count:

33

Contact:

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Activities

  • Workshop Generating Optimal Paths in Humanoid and Industrial Robotics at IEEE Humanoids 2012 , Osaka, Japan, Nov 29 (organized by Katja Mombaur, Martin Felis, Florent Lamiraux and Antonio El Khoury)

    Scope: Humanoid and industrial robots are required to perform motions in more and more complex and constantly changing environments. The generation of the best possible robot motion that does not violate any constraints imposed by the environment is a challenging and important problem in both humanoid and industrial robotics. There are two different algorithmic approaches that both address this problem from different perspectives, namely path planning and numerical optimal control. Path planning is mainly interested in the determination of a feasible path in very complex environments based on geometric and kinematic models. Numerical optimal control techniques are capable to generate optimal trajectories for robot manipulators or humanoid robots taking into account the dynamics; however the treatment of a large number of environmental constraints giving rise to many local minima makes the problems very hard, if not impossible, to solve. The goal of this workshop is to gather researchers working in path planning with researchers working in optimal control, in order to give an overview of the state of the art and to discuss the issues related to combining algorithms from both fields

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Model-Based Optimization for Robotics

The scope of the IEEE RAS TC Model-based optimization for robotics is the development and application of model-based optimization techniques for the generation and control of dynamic behaviors in robotics and their practical implementation.

Robots and in particular humanoid robots are extremely complicated dynamical systems for which the generation of behaviors is no easy task, since the number of parameters to tune for a behavior is very high. But the challenges waiting for today's robots require them to automatically generate and control a wide range of behaviors in order to be more flexible and adaptive to changing environments. Optimization or optimal control offers an interesting way to generate behaviors automatically based on elementary principles (cost functions, constraints). Moore's law as well as recent developments in optimization algorithms and in particular real-time optimization make a wider application of algorithmic optimization a realistic option even for real-time control in complex robotic applications in the near future.

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Important research areas promoted by the TC Model-based optimization for Robotics include:

  • Optimization-based generation of robot trajectories using dynamical models of the robot and its environment
  • Improve the behavior style of robots by optimization, in particular for humanoid robots (induce natural behavior)
  • Online motion control using real-time model-based optimization and model predictive control / receding horizon control
  • Development of appropriate dynamical models for offline and online optimization
  • Learning /improving models during optimization
  • Inverse optimal control techniques for the identification of objective functions
  • Robust optimal control and refinement of optimal controls based on
  • actual experience
  • Combination of optimization and machine learning approaches
  • Combination of optimization and path planning methods

This interdisciplinary scope includes establishing bridges to the mathematical optimization community as well as to the field of biomechanics (to learn from biology and to identify optimality criteria) and to computer graphics (for promising optimization approaches on physically realistic models).

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News and announcements

www.tc-opt.uni-hd.de