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Humanoid Robots Learning to Walk Faster: From the Real World to Simulation and Back

Alon Farchy, Samuel Barrett, Patrick MacAlpine, and Peter Stone. Humanoid Robots Learning to Walk Faster: From the Real World to Simulation and Back. In Proc. of 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.
The videos referenced in the paper: original and optimized.

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Abstract

Simulation is often used in research and industry as a low cost, high efficiency alternative to real model testing. Simulation has also been used to develop and test powerful learning algorithms. However, parameters learned in simulation often do not translate directly to the application, especially because heavy optimization in simulation has been observed to exploit theinevitable simulator simplifications, thus creating a gap between simulation andapplication that reduces the utility of learning in simulation.This paper introduces Grounded Simulation Learning (GSL), an iterativeoptimization framework for speeding up robot learning using an imperfect simulator. In GSL, a behavior is developed on a robot and then repeatedly: 1) the behavior is optimized in simulation; 2) the resulting behavior is testedon the real robot and compared to the expected results from simulation, and 3) the simulator is modified, using a machine-learning approach to come closer in line with reality. This approach is fully implemented and validated on the taskof learning to walk using an Aldebaran Nao humanoid robot. Starting from a set of stable, hand-coded walk parameters, four iterations of this three-step optimization loop led to more than a 25% increase in the robot's walking speed.

BibTeX

@InProceedings{AAMAS13-Farchy,
  author = {Alon Farchy and Samuel Barrett and Patrick MacAlpine and Peter Stone},
  title = {Humanoid Robots Learning to Walk Faster: From the Real World to Simulation and Back},
  booktitle = {Proc. of 12th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {Saint Paul, Minnesota, USA},
  month = {May},
  year = {2013},
  wwwnote={The videos referenced in the paper: <a href="http://youtu.be/grlceQkBTxw">original</a> and <a href="http://youtu.be/nGc127yYoSs">optimized</a>.},
  abstract={
    Simulation is often used in research and industry as a low cost, high 
efficiency alternative to real model testing. Simulation has also been used to 
develop and test powerful learning algorithms.  However, parameters learned in 
simulation often do not translate directly to the application, especially 
because  heavy optimization in simulation has been observed to exploit the
inevitable simulator simplifications, thus creating a gap between simulation and
application that reduces the utility of learning in simulation.
This paper introduces Grounded Simulation Learning (GSL), an iterative
optimization framework for speeding up robot learning using an imperfect 
simulator.  In GSL, a behavior is developed on a robot and then repeatedly: 
1) the behavior is optimized in simulation; 2) the resulting behavior is tested
on the real robot and compared to the expected results from simulation, and 3) 
the simulator is modified, using a machine-learning approach to come closer in 
line with reality.  This approach is fully implemented and validated on the task
of learning to walk using an Aldebaran Nao humanoid robot.  Starting from a set 
of stable, hand-coded walk parameters, four iterations of this three-step 
optimization loop led to more than a 25\% increase in the robot's walking speed.
  },
}

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