2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots
San Francisco, California USA
13-16 December 2016
CALL FOR PARTICIPATION
Early Registration Deadline: 31 October 2016
After a very selective review process, approximately half of the paper submissions were rejected, and we are looking forward to a high-quality, single track program of IEEE SIMPAR 2016.
Combining Optimal Control, Reinforcement Learning and Movement
Primitives to Achieve Better Robot Motions
Organizer: Katja Mombaur
Grand Challenges in Robotic Simulation
Organizer: Evan Drumwright
Modeling and Simulating Mechanical Rigid-Body Systems Using Siconos
Organizers: Vincent Acary and Stephen Sinclair
The Role of Simulation in Robot Programming
Organizers: Maria Gini and Enrico Pagello
Technical tours will be held on 17 December 2016 (incl. robotics lab tours at UC Berkeley and Stanford University).
VENUE, ACCOMMODATION, AND SOCIAL EVENTS
The Parc 55 – A Hilton Hotel
We offer discounted room rates for SIMPAR attendees.
On 14 December 2016, all conference attendees will be able to enjoy a beautiful dinner cruise on the San Francisco Bay.
The 2016 International Workshop on the Algorithmic Foundations of Robotics (WAFR) will take place in San Francisco from 18-20 December 2016, adjacent to SIMPAR 2016.
SCOPE AND TOPICS
3D robot simulation and mathematical modeling of robots
Learning from simulation
Reliability, scalability and validation of robot simulation
Simulated sensors and actuators
Machine learning for robotics applications
Offline simulation of robot design
Online simulation with real-time constraints
Simulation with software/hardware in the loop
Middleware for robotics
Modeling framework for robots and environments
Testing and validation of robot software
Standardization for robotic services
Communication infrastructures in distributed robotics
Interaction between sensor networks and robots
Human robot interaction and collaboration
Simulation of multi-robot systems
Model-based optimization and optimal control
Model predictive control
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