Improving Local Trajectory Optimisation using Probabilistic Movement Primitives

Babu, Ashith, Lightbody, Peter, Das, Gautham , Liu, Pengcheng, Gomez-Gonzalez, Sebastian and Neumann, Gerhard (2019) Improving Local Trajectory Optimisation using Probabilistic Movement Primitives. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 03-08 November, Macau, China, China.

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Improving Local Trajectory Optimisation using Probabilistic Movement Primitives
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Local trajectory optimisation techniques are a powerful tool for motion planning. However, they often get stuck in local optima depending on the quality of the initial solution and consequently, often do not find a valid (i.e. collision free) trajectory. Moreover, they often require fine tuning of a cost function to obtain the desired motions. In this paper, we address both problems by combining local trajectory optimisation with learning from demonstrations. The human expert demonstrates how to reach different target end-effector locations in different ways. From these demonstrations, we estimate a trajectory distribution, represented by a Probabilistic Movement Primitive (ProMP). For a new target location, we sample different trajectories from the ProMP and use these trajectories as initial solutions for the local optimisation. As the ProMP generates versatile initial solutions for the optimisation, the chance of finding poor local minima is significantly reduced. Moreover, the learned trajectory distribution is used to specify the smoothness costs for the optimisation, resulting in solutions of similar shape as the demonstrations. We demonstrate the effectiveness of our approach in several complex obstacle avoidance scenarios.

Keywords:Collision avoidance, End effectors, Probabilistic trajectory planning
Subjects:H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G320 Probability
Divisions:College of Science > School of Computer Science
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ID Code:40837
Deposited On:28 May 2020 10:43

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