Katja Mombaur is Full Professor and Canada Excellence Research Chair in Human-Centred Robotics and Machine Intelligence at the University of Waterloo in Canada. Until early 2020 she was a Full Professor and head of the Optimization, Robotics & Biomechanics (ORB) group at Heidelberg University, Germany. She holds a diploma degree in Aerospace Engineering from the University of Stuttgart and a PhD degree in Mathematics from Heidelberg University. She was a postdoctoral researcher in the Robotics Lab at Seoul National University, South Korea, and spent two years as a visiting researcher in the Robotics department of LAAS-CNRS in Toulouse, France. Since 2022 she is the Secretary of the IEEE RAS ExCom. Her research focuses on understanding human movement by a combined approach of model-based optimization and biomechanical experiments and using this knowledge to improve motions of humanoid robots and the interactions of humans with exoskeletons, prostheses, and external physical devices.
Talk # 1
Model-based optimization for improving the motion intelligence of human-centred robots
Human-centred robots have the potential to support and facilitate people’s lives, ranging from improved well-being and increased independence to reduced risk or harm and a removal of boring jobs. They can take the form of humanoid robots, wearable robots or other types of mobility assistance robots and have to enter in in close physical interactions with humans or support them physically. For this, human-centred robots require motion intelligence or embodied intelligence that makes the robot aware of how it moves in and interacts with its dynamic environment and with humans. In addition to biomechanical studies of human behavior, model-based optimization or optimal control is a widely used approach for generating and controlling motions of human-centered robots. Optimization can tackle the challenges of such systems which include a high complexity, redundancy, underactuation, and a high risk of instability and falls.
In this talk, I will present different examples from my research group on using model-based optimal control to control different humanoid robot platform and to improve the design and control of wearable robots for the lower limbs and the lower back and other assistive devices. I will discuss different levels of modeling robots – and in some cases also the interacting humans - to address specific research questions. In addition, I will discuss possible combinations of optimal control methods with reinforcement learning and movement primitive approaches to reduce computation times and improve robot control.
Talk # 2
What do we optimize? Inverse Optimal Control as a Tool to Understand Human
Gaining a fundamental understanding of the movements of the human body has long been an important research topic in biomechanics, sports science, physiology, neuroscience, computer animation and many areas of robotics and human-robot interaction. How do humans choose their motions out of the infinite number of ways to perform a given task? And how do motions change based on the situation, or based on the person’s age, training level or medical condition? It is a common assumption that motions of humans and animals – similar to many other processes in nature - are performed in an optimal way due to evolution, learning and training. Optimality principles can be found in the mechanical properties of the executed movements, but also in the closed loop sensory motor system. However, the particular criterion optimized is highly dependent on the specific case and situation and not easy to determine.
In this talk, I discuss inverse optimal control as a very promising systematic approach to identify the underlying optimality principles of human movement. Starting from (partial) motion capture data of a specific human movement or a set of movements of multiple subjects and subject-specific mathematical models, inverse optimal control tells us which objective function – or typically combination of multiple objective functions - gives the closest approximation of the recorded data. I will present algorithmic approaches for solving inverse optimal control as well as a number of examples, including walking on different terrains, running motions of amputees and non-amputees, painting, lifting motions with and without back pain, and interactions with robotic manipulandum.
Inverse optimal control has lead to very promising results in these biomechanical studies, and certainly has a huge potential for a much wider systematic analysis of data in other types of biological processes.
Primary Keywords: Human Detection and Tracking; Humanoid and Bipedal Locomotion; Humanoid Robots; Optimization and Optimal Control; Physical Human-Robot Interaction; Physically Assistive Devices; Prosthetics and Exoskeletons; Rehabilitation Robotics; Simulation and Animation; Wearable Robots
Secondary Keywords: Biologically-Inspired Robots; Biomimetics; Dual Arm Manipulation; Dynamics; Gesture, Posture and Facial Expressions; Human Factors and; Human-in-the-Loop; Human Performance Augmentation; Human-Centered Robotics; Model Learning for Control; Motion Control; Natural Machine Motion; Passive Walking
