5,516 research outputs found
Agile Autonomous Driving using End-to-End Deep Imitation Learning
We present an end-to-end imitation learning system for agile, off-road
autonomous driving using only low-cost sensors. By imitating a model predictive
controller equipped with advanced sensors, we train a deep neural network
control policy to map raw, high-dimensional observations to continuous steering
and throttle commands. Compared with recent approaches to similar tasks, our
method requires neither state estimation nor on-the-fly planning to navigate
the vehicle. Our approach relies on, and experimentally validates, recent
imitation learning theory. Empirically, we show that policies trained with
online imitation learning overcome well-known challenges related to covariate
shift and generalize better than policies trained with batch imitation
learning. Built on these insights, our autonomous driving system demonstrates
successful high-speed off-road driving, matching the state-of-the-art
performance.Comment: 13 pages, Robotics: Science and Systems (RSS) 201
Informative Path Planning for Active Field Mapping under Localization Uncertainty
Information gathering algorithms play a key role in unlocking the potential
of robots for efficient data collection in a wide range of applications.
However, most existing strategies neglect the fundamental problem of the robot
pose uncertainty, which is an implicit requirement for creating robust,
high-quality maps. To address this issue, we introduce an informative planning
framework for active mapping that explicitly accounts for the pose uncertainty
in both the mapping and planning tasks. Our strategy exploits a Gaussian
Process (GP) model to capture a target environmental field given the
uncertainty on its inputs. For planning, we formulate a new utility function
that couples the localization and field mapping objectives in GP-based mapping
scenarios in a principled way, without relying on any manually tuned
parameters. Extensive simulations show that our approach outperforms existing
strategies, with reductions in mean pose uncertainty and map error. We also
present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation
Letters (and IEEE International Conference on Robotics and Automation
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