9,853 research outputs found
Learning Generalized Reactive Policies using Deep Neural Networks
We present a new approach to learning for planning, where knowledge acquired
while solving a given set of planning problems is used to plan faster in
related, but new problem instances. We show that a deep neural network can be
used to learn and represent a \emph{generalized reactive policy} (GRP) that
maps a problem instance and a state to an action, and that the learned GRPs
efficiently solve large classes of challenging problem instances. In contrast
to prior efforts in this direction, our approach significantly reduces the
dependence of learning on handcrafted domain knowledge or feature selection.
Instead, the GRP is trained from scratch using a set of successful execution
traces. We show that our approach can also be used to automatically learn a
heuristic function that can be used in directed search algorithms. We evaluate
our approach using an extensive suite of experiments on two challenging
planning problem domains and show that our approach facilitates learning
complex decision making policies and powerful heuristic functions with minimal
human input. Videos of our results are available at goo.gl/Hpy4e3
URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments
A major challenge with off-road autonomous navigation is the lack of maps or
road markings that can be used to plan a path for autonomous robots. Classical
path planning methods mostly assume a perfectly known environment without
accounting for the inherent perception and sensing uncertainty from detecting
terrain and obstacles in off-road environments. Recent work in computer vision
and deep neural networks has advanced the capability of terrain traversability
segmentation from raw images; however, the feasibility of using these noisy
segmentation maps for navigation and path planning has not been adequately
explored. To address this problem, this research proposes an uncertainty-aware
path planning method, URA* using aerial images for autonomous navigation in
off-road environments. An ensemble convolutional neural network (CNN) model is
first used to perform pixel-level traversability estimation from aerial images
of the region of interest. The traversability predictions are represented as a
grid of traversal probability values. An uncertainty-aware planner is then
applied to compute the best path from a start point to a goal point given these
noisy traversal probability estimates. The proposed planner also incorporates
replanning techniques to allow rapid replanning during online robot operation.
The proposed method is evaluated on the Massachusetts Road Dataset, the
DeepGlobe dataset, as well as a dataset of aerial images from off-road proving
grounds at Mississippi State University. Results show that the proposed image
segmentation and planning methods outperform conventional planning algorithms
in terms of the quality and feasibility of the initial path, as well as the
quality of replanned paths
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