Hybrid control trajectory optimization under uncertainty

Pajarinen, J. and Kyrki, V. and Koval, M. and Srinivasa, S and Peters, J. and Neumann, G. (2017) Hybrid control trajectory optimization under uncertainty. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 24 - 28 September 2017, September 24–28, 2017, Vancouver, BC, Canada.

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Item Type:Conference or Workshop contribution (Paper)
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Abstract

Trajectory optimization is a fundamental problem in robotics. While optimization of continuous control trajectories is well developed, many applications require both discrete and continuous, i.e. hybrid controls. Finding an optimal sequence of hybrid controls is challenging due to the exponential explosion of discrete control combinations. Our method, based on Differential Dynamic Programming (DDP), circumvents this problem by incorporating discrete actions inside DDP: we first optimize continuous mixtures of discrete actions, and, subsequently force the mixtures into fully discrete actions. Moreover, we show how our approach can be extended to partially observable Markov decision processes (POMDPs) for trajectory planning under uncertainty. We validate the approach in a car driving problem where the robot has to switch discrete gears and in a box pushing application where the robot can switch the side of the box to push. The pose and the friction parameters of the pushed box are initially unknown and only indirectly observable.

Keywords:Trajectory Optimization, Hybrid Control
Subjects:H Engineering > H671 Robotics
Divisions:College of Science > School of Computer Science
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ID Code:28257
Deposited On:25 Aug 2017 09:10

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