CALL FOR PAPERS:
ICRA 19 Workshop on Algorithms and Architectures for Learning-in-the-Loop
Systems in Autonomous Flight
Montreal, Canada
WEBPAGE:
https://uav-learning-icra.github.io/2019/
<https://uav-learning-icra.github.io/2019/>
DATES:
Paper submission deadline: 7-Apr-2019
Author notification: 29-Apr-2019
OVERVIEW:
In past years, model-based techniques have successfully endowed aerial robots
with impressive capabilities like high-speed navigation through unknown
environments. However, task specifications, like goal positions, are often
still hand-engineered. Machine learning and deep learning have emerged as
promising tools for higher-level autonomy, but are more difficult to analyze
and implement in real-time. Furthermore, maintaining high thrust-to-weight
ratios for agility directly contradicts the need to carry sensor and
computation resources, making hardware and software architecture equally
crucial decisions.
This workshop aims to bring together researchers in the complementary elds of
aerial robotics, learning, and systems to discuss the following themes:
Learning for flight - How should learning be incorporated into UAVs'
perception-action loops?
Structure in learning - How can models, structure, and priors enhance learning
on UAVs?
Performance guarantees - How can we analyze closed-loop performance of
learning-in-the-loop systems
Software+hardware co-design - How can we implement learning algorithms on
resource-constrained UAVs? How should we simultaneously optimize algorithms and
hardware choices to create lightweight, but highly-capable, UAVs?
SUBMISSION INFORMATION:
We are soliciting 4-page papers (not including references) with up to a
2-minute accompanying video. We strongly prefer work featuring experimental
validation (including initial preliminary results) but will consider
simulation-only papers provided they convincingly address why the utilized
simulator is a compelling representation of real-world conditions. We
especially encourage papers that share valuable “failure analyses" or “lessons
learned" that would benefit the community.
SCOPE AND TOPICS:
Topics of interest include (but are not limited to):
- Combining model-based and model-free methods for autonomous flight
- Online learning and adaptation in mapping, perception, planning, and/or
control for UAVs
- End-to-end learning of perception-action loops for flight
-Sample ecient learning on flying robots
- Learning for high-level autonomy in applications such as (but not limited to)
disaster response, cinematography, search and rescue, environmental monitoring,
aerial manipulation, agriculture, and inspection
- Closed-loop analysis learning-in-the-loop systems
- Metrics for evaluating the benefits of incorporating learning into
perception-action loops or
incorporating models into learning algorithms
- Challenges implementing learning algorithms in real-time on sensorimotor
systems
- Novel architectures that use multi-agent networks or the cloud to
decentralize demanding computations
- Insights into architecture design, system component choice, and
implementation details (including “failed designs") of real-time
learning-in-the-loop algorithms
ORGANIZERS:
Dr. Aleksandra Faust, Google Brain
Dr. Vijay Janapa Reddi, Harvard University
Dr. Angela Schoellig, University of Toronto
Dr. Sarah Tang, University of Pennsylvania/Nuro, Inc.
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