|Title||Deep sequential models for sampling-based planning|
|Publication Type||Conference Paper|
|Year of Publication||2018|
|Authors||Kuo, Y-L, Barbu, A, Katz, B|
|Conference Name||The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)|
|Conference Location||Madrid, Spain|
We demonstrate how a sequence model and asampling-based planner can influence each other to produceefficient plans and how such a model can automatically learnto take advantage of observations of the environment. Sampling-based planners such as RRT generally know nothing of theirenvironments even if they have traversed similar spaces manytimes. A sequence model, such as an HMM or LSTM, guidesthe search for good paths. The resulting model, called DeRRT∗,observes the state of the planner and the local environment tobias the next move and next planner state. The neural-network-based models avoid manual feature engineering by co-traininga convolutional network which processes map features andobservations from sensors. We incorporate this sequence modelin a manner that combines its likelihood with the existing biasfor searching large unexplored Voronoi regions. This leads tomore efficient trajectories with fewer rejected samples even indifficult domains such as when escaping bug traps. This modelcan also be used for dimensionality reduction in multi-agentenvironments with dynamic obstacles. Instead of planning in ahigh-dimensional space that includes the configurations of theother agents, we plan in a low-dimensional subspace relying onthe sequence model to bias samples using the observed behaviorof the other agents. The techniques presented here are general,include both graphical models and deep learning approaches,and can be adapted to a range of planners.
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