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This workshop brings together experts in the areas of Mathematics, Machine Learning, Perception, Communications, Sensors, Robotics and Control into one common forum to discuss state-of-the-art methods for detection, classification, and behavioral inference of remotely-sensed, multi-agent dynamical systems and their application in robotics.
This workshop brings together experts in the areas of Mathematics, Machine Learning, Perception, Communications, Sensors, Robotics and Control into one common forum to discuss state-of-the-art methods for detection, classification, and behavioral inference of remotely-sensed, multi-agent dynamical systems and their application in robotics.
Unsupervised learning of interaction dynamics and behavior from multi-agent observations has broad applications in physics, computer vision and robotics. Examples of multi-agent dynamic scenarios of interest in robotics include autonomous driving, livestock monitoring or competitive team games, such as the robocup. In all these problems, we can identify multiple agents (vehicles, animals, players, etc.) that dynamically interact with each other in complex ways that may be difficult to sense and analyze. Accurate modeling of their intended behavior and actions can improve not only a robot's capacity to perceive the world, as in SLAM, but also its capacity to make informed decisions and act accordingly.
A successful approach to these issues requires the use of advanced inference algorithms that utilize state-of-the-art machine learning, data analysis, and dynamical system modeling. In addition, specialized sensors, such as EO/IR or event-based cameras, may play an important role in the perception process by providing alternative modalities with contrasting and complementary capabilities that help discriminate characteristic actions. More importantly, the interplay between inference algorithms and sensors can prove critical in the development of successful robotic systems that are able to properly discern and identify multi-agent behaviors.
Invited Speakers
Invited Speakers
Harbir Antil
Harbir Antil
George Mason University
John Baras
John Baras
University of Maryland
Andrea Cavallaro
Andrea Cavallaro
Queen Mary University
Guillermo Gallego
Guillermo Gallego
TU Berlin
Stephanie Gil
Stephanie Gil
Harvard University
Jonathan How
Jonathan How
MIT
M. Ani Hsieh
M. Ani Hsieh
University of Pennsylvania
Isaac Kaminer
Isaac Kaminer
Naval Postgraduate School
Aljosa Osep
Aljosa Osep
TU Munich
Schedule
Schedule
SLOT 1: 2:00-3:45 pm (CEST) / 8:00-9:45am (ET) / 5:00-6:45 am (PT)
2:00-2:15 pm (CEST) / 8:00-8:15 am (ET) / 5:00-5:15 am (PT)
Introduction by the organizers
2:15-2:45 pm (CEST) / 8:15-8:45 am (ET) / 5:15-5:45 am (PT)
Andrea Cavallaro - Multi-modal learning for robot perception
2:45-3:15 pm (CEST) / 8:45-9:15 am (ET) / 5:45-6:15 am (PT)
Guillermo Gallego - Event-based vision in multi-agent scenarios
3:15-3:45 pm (CEST) / 9.15-9:45 am (ET) / 6:15-6:45 am (PT)
Aljosa Osep - Tracking Every Pixel and Object
Break (15min)
MAIN CONFERENCE SLOT: 4-7 pm (CEST) / 10 am- 1 pm(ET) / 7-10 am (PT)
Break (15min)
SLOT 2: 7:15-9:15 pm (CEST) / 1:15-3:15 pm (ET) / 10:15 am- 12:15pm (PT)
7:15-7:45 pm (CEST) / 1:15-1:45 pm (ET) / 10:15-10:45 am (PT)
M. Ani Hsieh - Learning to Swarm Using Knowledge-based Neural Ordinary Differential Equations
7:45-8:15 pm (CEST) / 1:45-2:15 pm (ET) / 10:45-11:15 am (PT)
Contributed paper presentations
1 - Leveraging Specification Inference for Human Motion Prediction
Estefany Carrillo and Huan Xu
2 - Simultaneously learning safety margins and task parameters of multirobot systems
Jaskaran Singh Grover, Changliu Liu and Katia Sycara
3 - Multi-robot Implicit Control of Herds
Eduardo Sebastián, Eduardo Montijano and Carlos Sagüés
4 - Distributed Multi-Target Tracking in Camera Networks
Sara Casao, Ana Cristina Murillo and Eduardo Montijano
8:15-8:45 pm (CEST) / 2:15-2:45 pm (ET) / 11:15-11:45 am (PT)
Stephanie Gil - Situational Awareness and Secure Coordination for Multi-Robot Teams
8:45-9:15 pm (CEST) / 2:45-3:15 pm (ET) / 11:45 am-12:15 pm (PT)
Jonathan How - SLAM & RL Solutions for Multiagent Systems
Break (15min)
SLOT 3: 9:30-11:30 pm (CEST) / 3:30-5:30 pm (ET) / 12:30-2:30 pm (PT)
9:30-10:00 pm (CEST) / 3:30-4:00 pm (ET) / 12:30-1:00 pm (PT)
10:00-10:30 pm (CEST) / 4:00-4:30 pm (ET) / 1:00-1:30 pm (PT)
Harbir Antil - Optimization Based Deep Neural Networks with Memory
10:30-11:00 pm (CEST) / 4:30-5:00 pm (ET) / 1:30-2:00 pm (PT)
11:00-11:30 pm (CEST) / 5.00-5:30 pm (ET) / 2:00-2:30 pm (PT)
Final discussions and workshop closure
Organizers
Organizers
Eduardo Montijano
Eduardo Montijano
Universidad de Zaragoza
George Stantchev
George Stantchev
US Naval Research Laboratory
Colin
Olson
Colin
Olson
US Naval Research Laboratory
Ana Cristina Murillo
Ana Cristina Murillo
Universidad de Zaragoza
Margarita
Chli
Margarita
Chli
ETH Zurich
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