Model-based Optimization for Robotics

IEEE RAS Technical Committee


The scope of this IEEE RAS Technical Committee is the development and application of model-based optimization techniques for the generation and control of dynamic behaviors in robotics and their practical implementation. Optimization approaches are in principle applicable to any type of robot behavior, but are most interesting for robots with complex dynamics, such as humanoid robots or for high speeds, such as in (mobile) manipulation.


Important research areas 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).


We will also cooperate with other IEEE RAS TCs, such as the TC Humanoid Robotics or the TC Mobile Manipulation, to promote the used of optimization to particular application areas, or the TCs on Robot Learning or on Planning and Control to explore combinations of optimization methods with other types of algorithms.