4 research outputs found
Active Information Gathering for Long-Horizon Navigation Under Uncertainty by Learning the Value of Information
We address the task of long-horizon navigation in partially mapped
environments for which active gathering of information about faraway unseen
space is essential for good behavior. We present a novel planning strategy
that, at training time, affords tractable computation of the value of
information associated with revealing potentially informative regions of unseen
space, data used to train a graph neural network to predict the goodness of
temporally-extended exploratory actions. Our learning-augmented model-based
planning approach predicts the expected value of information of revealing
unseen space and is capable of using these predictions to actively seek
information and so improve long-horizon navigation. Across two simulated
office-like environments, our planner outperforms competitive learned and
non-learned baseline navigation strategies, achieving improvements of up to
63.76% and 36.68%, demonstrating its capacity to actively seek
performance-critical information.Comment: Submitted at IROS'24. arXiv admin note: text overlap with
arXiv:2307.1450
Improving Reliable Navigation under Uncertainty via Predictions Informed by Non-Local Information
We improve reliable, long-horizon, goal-directed navigation in
partially-mapped environments by using non-locally available information to
predict the goodness of temporally-extended actions that enter unseen space.
Making predictions about where to navigate in general requires non-local
information: any observations the robot has seen so far may provide information
about the goodness of a particular direction of travel. Building on recent work
in learning-augmented model-based planning under uncertainty, we present an
approach that can both rely on non-local information to make predictions (via a
graph neural network) and is reliable by design: it will always reach its goal,
even when learning does not provide accurate predictions. We conduct
experiments in three simulated environments in which non-local information is
needed to perform well. In our large scale university building environment,
generated from real-world floorplans to the scale, we demonstrate a 9.3\%
reduction in cost-to-go compared to a non-learned baseline and a 14.9\%
reduction compared to a learning-informed planner that can only use local
information to inform its predictions.Comment: IROS 202