Planning under Uncertainties: Closed-Loop Sensitivity in Robotic

Abstract: Addressing uncertainties is a key challenge in robotics, particularly as robotic systems are increasingly deployed in complex real-world scenarios. Uncertainties in robot dynamics, sensor noise, and environmental conditions can significantly impact planned trajectories, jeopardizing task execution. This seminar explores recent advancements in robust motion planning, focusing on the concept of closed-loop state sensitivity: a powerful tool for assessing how variations in model parameters impact system behavior under feedback control. The seminar will cover innovative methods for shaping system trajectories to enhance robustness by minimizing sensitivity. Following this, the concept of uncertainty tubes will be introduced, with an emphasis on their mathematical foundation and effectiveness in constraining deviations from planned trajectories. These approaches have been validated and tested on various robotic platforms, demonstrating their practical applicability and reliability in real-world scenarios.

Bio: Andrea Pupa is a Postdoctoral Researcher at the Department of Sciences and Methods for Engineering at the University of Modena and Reggio Emilia. He received his B.Sc. in Mechanical Engineering from the Polytechnic University of Milan in 2016 and his M.Sc. in Mechatronic Engineering from the University of Modena and Reggio Emilia in 2018. In the same institute, he received his PhD in Robotics Engineering in 2023. He was the recipient of the Italian Mechatronics Award in 2022 for his research project conducted in collaboration with the companies IMA S.p.A. and Datalogic S.p.A., which was part of the Rossini European Project. His research interests are primarily focused on safety-aware control techniques and dynamic task scheduling to enhance the efficiency of collaborative robotics within industrial settings. Additionally, he specializes in the use of closed-loop sensitivity to robustify robot behaviour in the presence of uncertainties.