Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces

@article{Mcconachie2020LearningWT,
  title={Learning When to Trust a Dynamics Model for Planning in Reduced State Spaces},
  author={Dale Mcconachie and Thomas Power and P. Mitrano and Dmitry Berenson},
  journal={IEEE Robotics and Automation Letters},
  year={2020},
  volume={5},
  pages={3540-3547}
}
When the dynamics of a system are difficult to model and/or time-consuming to evaluate, such as in deformable object manipulation tasks, motion planning algorithms struggle to find feasible plans efficiently. Such problems are often reduced to state spaces where the dynamics are straightforward to model and evaluate. However, such reductions usually discard information about the system for the benefit of computational efficiency, leading to cases where the true and reduced dynamics disagree on… 

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