Motion as an Information Signal in Physical Human-Robot Interaction

ABSTRACT:  Robotics and haptics have the potential to enhance human performance and learning as well as provide unique insight into neuromotor function through sensing and quantification of human motion. At the same time, human behavior can inform the development of control strategies for complex tasks and human-robot interactions. The methods used for evaluation of motion greatly influences our ability to recognize the effects of assistance and training from a statistical standpoint, but more importantly, the mathematical structure imposed by unique measures of motion quality has significant impact on the algorithmic tools that are available to manage the interactions between robots and humans. This presentation will discuss alternatives to traditional measures of motion (e.g., energy or error) for quantifying motion quality and synthesizing controls during physical human-robot interaction.

BIOGRAPHY:  Katie Fitzsimons received a BS in Mechanical Engineering from Michigan State University in 2013, an MS in Mechanical Engineering from Northwestern University in 2017, and a PhD from Northwestern in 2020. Dr. Fitzsimons’ research interests lie at the interaction between humans and autonomous systems at both the level of an individual human-robot pair and the broader exchange between the fields of human motion analysis and robotic control. She was awarded the National Science Foundation Graduate Research Fellowship in 2014 as an undergraduate and was awarded the National Defense Science and Engineering Graduate Fellowship in 2016. Dr. Fitzsimons joined the Mechanical Engineering Department at Penn State this past January. Further information about her lab can be found on the Human-Centered Robotics Lab Website:


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Media Contact: Serena Sidwell



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