Ruprecht-Karls-Universität Heidelberg
Optimization in Robotics and Biomechanics

Workshop @ SIMPAR 2016

 

 

Organizer:

The workshop is organized within the European FP7 project KoroiBot and the IEEE RAS Technical Committee Model-based Optimization for Robotics.

Time and Place:

  • December 13, 2016, full day: 8:45 - 17:00
  • Park 55 Hotel, San Fracisco, California, USA, room Cyril Magnin 1
  • For detailed programm, click here
  • More information about the SIMPAR onference at simpar.org

Summary:

Generating and controlling motions for complex robot systems such as humanoid robots is a challenging task. Model-based optimization, reinforcement learning and movement primitives represent three different bio-inspired approaches to tackle this issue. In this workshop, state of the art methods of all three fields are presented, and a special focus is put on how to best combine them to take the advantages of all approaches. Particular attention is paid to the application of whole-body motions wit changing contacts such as walking and balancing motions. Optimal control takes models of the robots at different levels of complexity as well as environmental constraints into account and can be used to exploit the physical limits of a robot. Optimal control can be performed in the offline and online modes (the latter often referred to as model-predictive control), but in all cases the model-reality mismatch has to be taken care of. Reinforcement learning can be performed without any model, but based on the real system, but for complex systems it may be very difficult to find feasible solutions that are feasible at all, so many iterations may fail. Movement primitives are very common in robotics, especially for transferring motions from humans to robots, with many fundamentally different variants of primitives around. The workshop will present latest research in all of these areas as well as new ideas for combinations and their applications to challenging robotics problems

Topics:

  • Challenging types of whole-body robot motions that require sophisticated motion generation and control methods
  • State of the art methods for optimization / optimal control for multi-phase motion generation
  • State of the art methods for (nonlinear) model-predictive control / online optimization of motions
  • State of the art methods for reinforcement learning 
  • Model-free vs. model-based learning
  • Different types of movement primitives (kinematic, dynamic, muscle synergies) and different types of segmentation approaches and retargeting
  • Different approaches to transfer primitives from humans to robots
  • Dynamic and kinematic models of humans and robots
  • Model reduction for optimization
  • Combining optimization & learning methods
  • Combining optimization & movement primitives
  • Shared open questions in both reinforcement learning and optimal control approaches
  • Considering multibody dynamics and constraints in movement primitives
  • Successful implementation of new (combined) methods to robots

Confirmed workshop speakers

  • Chris Atkeson, CMU
  • Hongkai Dai, Toyota Research Institute / MIT
  • Tamar Flash, Weizmann Institute
  • Dominik Endres, University of Marburg
  • Martin Giese, University of Tübingen
  • Sergey Levine, Berkeley
  • Richard Longman, Columbia University
  • Katja Mombaur, Heidelberg University

Workshop Program

Participation

For participaon at the workshop, please register via the SIMPAR conference.

Audience

 

The goal of this workshop is to gather specialists from robot optimization, robot learning and movement primitives in robotics to present the state of the art of methods in all fields, exchange ideas, discuss similarities and differences of the three approaches as well as a potential combination of the methods. 

 

 

K. Mombaur, orb@uni-hd.de
Last Update: 09.12.2016 - 19:07

The photographs in the header of this webpage have been taken at the Musee de l'Automate in Souillac, France