[robotics-worldwide] [meetings] SIMPAR 2016 Workshop on Optimal control, reinforcement learning & movement primitives

Katja Mombaur katja.mombaur at iwr.uni-heidelberg.de
Sat Oct 29 09:51:33 PDT 2016


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SIMPAR 2016 Workshop – Call for Posters & Participation

Combining optimal control, reinforcement learning and movement
primitives to achieve better robot motions

Dec 13, 2016, San Francisco

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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 with 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 of interest:
- 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
- Common features and differences between inverse optimal control and 
inverse reinforcement learning
- Considering multibody dynamics and constraints in movement primitives
- Successful implementation of new (combined) methods to robots


Organizer: Katja Mombaur, Heidelberg University


More information about the workshop at
https://urldefense.proofpoint.com/v2/url?u=http-3A__www.orb.uni-2Dhd.de_conferences-2Dworkshops_simpar2016_&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=LjFSJYvLL8FEMxRjqTHeraPBOCzvS0q9Rou0svs5Mm0&s=mF5K651qy1upBoWnuX6SoioQGCOxv5t5q9MqkWeoBDs&e= 

More information about SIMPAR at
https://urldefense.proofpoint.com/v2/url?u=http-3A__simpar2016.org_&d=DgIDaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=LjFSJYvLL8FEMxRjqTHeraPBOCzvS0q9Rou0svs5Mm0&s=9iLFOBgvPwU6VzPCv7t_ivWuKv-FyBzLCXIm4HSkGHI&e= 


Please send your abstracts (1 page double column IEEE style, 1 picture) to
kmombaur at uni-hd.de <mailto:kmombaur at uni-hd.de>

Deadline: Nov 10, 2016
Notification: Nov 11, 2016











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