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

[Sarah Tang]

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, 11:59PM AoE
Author notification: 29-Apr-2019

SUBMISSION INFORMATION:
Submission link: https://easychair.org/my/conference.cgi?conf=lsaf19
 <https://easychair.org/my/conference.cgi?conf=lsaf19>Detailed submission instructions: https://uav-learning-icra.github.io/2019/
 <https://uav-learning-icra.github.io/2019/>Questions can be directed to: lsaf19 at easychair.org <mailto:lsaf19 at easychair.org>
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 fields of aerial robotics, learning, and systems to discuss the following themes:
- Learning for autonomous robots - How should learning be incorporated into robots' perception-action loops?
- Structure in learning - How can models, structure, and priors enhance learning on robots?
- 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 welcome work with experimental validation (including initial preliminary results) or addressing challenges associated with real-world implementation. We also welcome simulation-only papers that convincingly address why the utilized simulator is a compelling representation of real-world conditions and papers with validation on other robotics platforms that could be applied to UAVs. We especially encourage papers that share valuable “failure analyses" or “lessons learned" that would benefit the community. We welcome work at all stages of research, including work-in-progress and recently accepted or published results.

SCOPE AND TOPICS:
Topics of interest include (but are not limited to):
- Combining model-based and model-free methods for autonomous robotics
- Online learning and adaptation in mapping, perception, planning, and/or control for UAVs
- End-to-end learning of perception-action loops for flight
- Sample efficient 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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