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

Sarah Tang sytang at alumni.upenn.edu
Thu Mar 28 05:47:49 PDT 2019

ICRA 19 Workshop on Algorithms and Architectures for Learning-in-the-Loop Systems in Autonomous Flight
Montreal, Canada

https://urldefense.proofpoint.com/v2/url?u=https-3A__uav-2Dlearning-2Dicra.github.io_2019_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=CtukSX4G0Jza3dsxoFYzLMbtoeN9THPC_rlb0g2A1KE&s=bTk-nSIGJd0F9MiudQk_C0W0k9bkxLtKvmUX3nvq2gI&e= <https://urldefense.proofpoint.com/v2/url?u=https-3A__uav-2Dlearning-2Dicra.github.io_2019_&d=DwIFaQ&c=clK7kQUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=0w3solp5fswiyWF2RL6rSs8MCeFamFEPafDTOhgTfYI&m=CtukSX4G0Jza3dsxoFYzLMbtoeN9THPC_rlb0g2A1KE&s=bTk-nSIGJd0F9MiudQk_C0W0k9bkxLtKvmUX3nvq2gI&e=>
Paper submission deadline: 7-Apr-2019
Author notification: 29-Apr-2019

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?

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. 

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

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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