[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

https://urldefense.proofpoint.com/v2/url?u=https-3A__uav-2Dlearning-2Dicra.github.io_2019_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=GdGORiXEY8jxMaN3wJ6dZgSSCr7_grZwMt9WgyzMcug&s=lleOR7eggm5tOd1yepBBjXxQAvG0vRGUfRSlsN-DC1o&e=

SUBMISSION INFORMATION:

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
community.

OVERVIEW:

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?

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:

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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