Control of Dynamically Complex Objects: Stability and Predictability

Control of Dynamically Complex Objects: Stability and Predictability

Presenter: Dagmar Sternad, University Distinguished Professor, Northeastern University

Abstract:

Extending capabilities with tools has been fundamental to human evolution and the advance of robotic devices presents the next stage in human augmentation. However, successfully incorporating an artificial limb into functional multi-degree-of-freedom actions remains a challenge for both the individual and the scientist/engineer. How can humans exploit the functionality of external devices, both active and passive? To date, there is still surprisingly little understanding of how humans control their interaction with the vast variety of objects ubiquitous in everyday life. This research studies human control at the example of transporting a “cup of coffee”, a physical interaction between the hand and a dynamically complex object. Using a virtual set-up with a robotic interface, participants moved a simple cart-and-pendulum model on a horizontal line; the pendulum bob represented the liquid moving inside a cup defined by the bob’s semicircular path. Despite its vast simplification from the real 3D fluid dynamic problem, the model task retained core challenges: it is nonlinear with potentially chaotic behavior presenting interaction forces that are difficult to predict. A series of studies revealed that humans developed strategies that prioritized stability and predictability. In fast point-to-point movements humans developed strategies with sufficient energy margins to preempt the risk of “spilling the coffee”. When a small perturbation was presented along the path, subjects learnt to stabilize their trajectories and attenuated the perturbations by moving through contraction regions of the free, unforced system. When moving the cup in a continuous rhythmic fashion, the nonlinear interactive dynamics became even more complex and unpredictable. Results showed that humans made the interactions more predictable, sacrificing solutions that would economize on the expended force. When subjects were allowed to choose their own initial conditions, they learnt to initiate the trial to shorten the transient to reach a steady - predictable - state. These findings demonstrate that humans are sensitive to stability and predictability of the task and exploit them to enable safe interaction with dynamically complex objects. These insights can serve as a basis to better understand and develop successful interaction of humans with both passive and active robotic devices.

Bio:

Dagmar Sternad is University Distinguished Professor in the Departments of Biology, Electrical and Computer Engineering, and Physics at Northeastern University. She received her BS in Movement Science and Linguistics from the Technical University and Ludwig Maximilians University of Munich, Germany, and her PhD in Experimental Psychology from the University of Connecticut. From 1995 until 2008, she was Assistant, Associate, and Full Professor at the Pennsylvania State University in Integrative Biosciences and Kinesiology. Since 2008, she holds the interdisciplinary appointment at Northeastern University and is also member of the Center for Interdisciplinary Research on Complex Systems at Northeastern. She received two university-wide recognitions for her teaching and dedication to students. Her research is documented in over 150 peer-reviewed publications, conference proceedings, book chapters, and several books. She gave close to 200 presentations at national and international venues. She has held editorial appointments in several scientific journals and was regular member of a NIH study section. Her research has been continuously supported by the National Institute of Health, National Science Foundation, American Heart Association, Office of Naval Research, and others.