Abstract
Soft robots are able to safely interact with delicate objects, absorb impacts without damage, and adapt to the shape of their environment, making them ideal for applications that require safe robot-human interaction. However, their use in real-world applications has been limited due to the difficulty of controlling them and hardware issues such as their low force output. In this talk, I’ll describe recent efforts to address these shortcomings. First, I’ll introduce a residual modeling and control approach that leverages Koopman operator theory to construct linear representations of nonlinear dynamical systems, enabling the use of efficient linear techniques to control soft robots. Using this Koopman-based approach, a pneumatically actuated soft arm was able to perform real-world manipulation tasks such as trajectory following, pick-and-place with a variable payload, and writing on a dry-erase board without undergoing any task-specific training. Second, I’ll introduce a model-based approach to increasing the force output of soft robots through localized-body stiffening. Taken together, these approaches improve the capabilities of soft robots, enabling their more widespread adoption in real-world applications.
Bio
Daniel Bruder is an Assistant Professor of Mechanical Engineering at the University of Michigan. Prior to this appointment, he was a postdoctoral fellow at Harvard University in the Microrobotics Lab supervised by Prof. Robert Wood. He received a Ph.D. in mechanical engineering from the University of Michigan in 2020 and a B.S. degree in engineering sciences from Harvard University in 2013. He is a recipient of the NSF Graduate Research Fellowship and the Richard and Eleanor Towner Prize for Outstanding Ph.D. Research. His research interests include the design, modeling, and control of robotic systems, especially soft robots.
