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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.

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

Harbir Antil

George Mason University

John Baras

University of Maryland

Andrea Cavallaro

Queen Mary University

Guillermo Gallego

TU Berlin

Stephanie Gil

Harvard University

Jonathan How

MIT

M. Ani Hsieh

University of Pennsylvania

Isaac Kaminer

Naval Postgraduate School

Aljosa Osep

TU Munich

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)

Isaac Kaminer and Abe Clark - Modeling Large-Scale Adversarial Swarm Engagements using Direct Methods of Optimal Control

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)

John Baras - From Copernicus-Brahe-Kepler to Swarms: Learning Composable Laws from Observed Trajectories

11:00-11:30 pm (CEST) / 5.00-5:30 pm (ET) / 2:00-2:30 pm (PT)

Final discussions and workshop closure

Organizers

Eduardo Montijano

Universidad de Zaragoza

George Stantchev

US Naval Research Laboratory

Colin
Olson

US Naval Research Laboratory

Ana Cristina Murillo

Universidad de Zaragoza

Margarita
Chli

ETH Zurich