89,544 research outputs found
Learning to Fly by Crashing
How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid
obstacles? One approach is to use a small dataset collected by human experts:
however, high capacity learning algorithms tend to overfit when trained with
little data. An alternative is to use simulation. But the gap between
simulation and real world remains large especially for perception problems. The
reason most research avoids using large-scale real data is the fear of crashes!
In this paper, we propose to bite the bullet and collect a dataset of crashes
itself! We build a drone whose sole purpose is to crash into objects: it
samples naive trajectories and crashes into random objects. We crash our drone
11,500 times to create one of the biggest UAV crash dataset. This dataset
captures the different ways in which a UAV can crash. We use all this negative
flying data in conjunction with positive data sampled from the same
trajectories to learn a simple yet powerful policy for UAV navigation. We show
that this simple self-supervised model is quite effective in navigating the UAV
even in extremely cluttered environments with dynamic obstacles including
humans. For supplementary video see: https://youtu.be/u151hJaGKU
Explainable Planning
As AI is increasingly being adopted into application solutions, the challenge
of supporting interaction with humans is becoming more apparent. Partly this is
to support integrated working styles, in which humans and intelligent systems
cooperate in problem-solving, but also it is a necessary step in the process of
building trust as humans migrate greater responsibility to such systems. The
challenge is to find effective ways to communicate the foundations of AI-driven
behaviour, when the algorithms that drive it are far from transparent to
humans. In this paper we consider the opportunities that arise in AI planning,
exploiting the model-based representations that form a familiar and common
basis for communication with users, while acknowledging the gap between
planning algorithms and human problem-solving.Comment: Presented at the IJCAI-17 workshop on Explainable AI
(http://home.earthlink.net/~dwaha/research/meetings/ijcai17-xai/). Melbourne,
August 201
Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing
Autonomous micro aerial vehicles still struggle with fast and agile
maneuvers, dynamic environments, imperfect sensing, and state estimation drift.
Autonomous drone racing brings these challenges to the fore. Human pilots can
fly a previously unseen track after a handful of practice runs. In contrast,
state-of-the-art autonomous navigation algorithms require either a precise
metric map of the environment or a large amount of training data collected in
the track of interest. To bridge this gap, we propose an approach that can fly
a new track in a previously unseen environment without a precise map or
expensive data collection. Our approach represents the global track layout with
coarse gate locations, which can be easily estimated from a single
demonstration flight. At test time, a convolutional network predicts the poses
of the closest gates along with their uncertainty. These predictions are
incorporated by an extended Kalman filter to maintain optimal
maximum-a-posteriori estimates of gate locations. This allows the framework to
cope with misleading high-variance estimates that could stem from poor
observability or lack of visible gates. Given the estimated gate poses, we use
model predictive control to quickly and accurately navigate through the track.
We conduct extensive experiments in the physical world, demonstrating agile and
robust flight through complex and diverse previously-unseen race tracks. The
presented approach was used to win the IROS 2018 Autonomous Drone Race
Competition, outracing the second-placing team by a factor of two.Comment: 6 pages (+1 references
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