Workshop
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Learning for Adaptive and Reactive Robot Control

Organised by the CHAIR theme Interpretable AI.

Led by: Nadia Figueroa, University of Pennsylvania.

Overview

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  • Date:Starts 4 June 2024, 10:00Ends 4 June 2024, 15:00
  • Location:
    ES51, Chalmers Campus Johanneberg
  • Language:English
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This is day 2 of the workshop "Learning for Adaptive and Reactive Robot Control". If you already registered for both days, you don´t need to register for day 2 again.

Photo of Nadia Figueroa

Boundary Function Learning and Extensions to Joint Space Control

Agenda:

  • 10:00 – 11:30 Reactive Joint-Space Control with Learned Boundary Functions 
  • 11:45 – 13:00 Lunch break 
  • 13:15 – 15:00 Constrained Passive Interaction Control in Joint Space 

Bio:

Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics Department at the University of Pennsylvania. She holds secondary appointments in Computer and Information Science and Electrical and Systems Engineering and is primary faculty of the GRASP laboratory.

She received a B.Sc. degree in Mechatronics from the Monterrey Institute of Technology, Mexico in 2007, an M.Sc. degree in Automation and Robotics from the Technical University of Dortmund, Germany in 2012 and a Ph.D. in Robotics, Control and Intelligent Systems at the Swiss Federal Institute of Technology in Lausanne, Switzerland (EPFL) in 2019. Prior to joining Penn, she was a Postdoctoral Associate in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology from 2020 to 2022.

Her research focuses on developing safety, control, estimation and learning methods for collaborative human-aware robotic systems: robots that can safely and efficiently interact with humans and other robots in the human-centric dynamic spaces we inhabit.

Her Ph.D thesis was a finalist for the Georges Giralt Ph.D. award in 2020 - the best European Ph.D. thesis in robotics, the ABB PhD Award and the EPFL Doctoral Distinction Award. Her work on multi-robot human collaboration was a finalist for the KUKA Innovation Award in 2017, Best Systems and Best Conference Paper Award and winner of the Best Student Paper Award at the 2016 Robotics: Science and Systems (RSS) Conference.

 

Previous topics, day 1 of the workshop, 28 May:

Learning and Safety for Adaptive and Reactive Robots 

  • Efficient Learning of Reactive Motion Policies with Stability and Convergence Guarantees
  • Passive Interaction Control and Boundary-based Safety Guarantees

 

Interpretable AI

Interpretable AI is an emerging field, focused on developing AI systems that are transparent and understandable to humans.