[robotics-worldwide] [meetings]: CfP: ICRA 19 Workshop on Algorithms and Architectures for Learning-in-the-Loop Systems in Autonomous Flight

Aleksandra Faust aleksandrafaust at gmail.com
Wed Mar 27 00:04:05 PDT 2019

Call for Papers / Deadline extension

Paper submission deadline:  7-Apr-2019

Author notification: 29-Apr-2019



We are soliciting 4-page papers (not including references) with up to a
2-minute accompanying video. We especially encourage papers that share
valuable “failure analyses" or “lessons learned" that would benefit the


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?


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

- 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


Aleksandra Faust, Google Brain

Vijay Janapa Reddi, Harvard University

Angela Schoellig, University of Toronto

Sarah Tang, University of Pennsylvania/Nuro, Inc.

Contact: lsaf19 at easychair.org

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