To make a future where robots are helpful and commonplace, we will need to consider how robots will physically interact with humans and their surroundings. Currently, most robot body plans are metal arms or plastic boxes on wheels, making it difficult to have safe yet rich interactions with their environments. In this talk, I argue that robots should be designed from a materials-centric approach to better enable these interactions. If core robotic features like actuation and perception can be directly incorporated into a robot’s materials, we could directly control the robot’s primary interface to the outside world.
Drawing from principles in mathematics and metamaterial design, I use auxetic materials as a case study to show how metamaterials can be explicitly designed as the foundation for a robot’s movement and sensing capabilities. I demonstrate the power of this approach by creating expanding modular robots with strength-to-weight ratios of 76x and developing a novel class of auxetics that make soft robotic grippers that are 20x more efficient than standard pneumatic versions. I also present a method for directly sensorizing metamaterial structures in general by embedding internal fluidic channels within the struts themselves as the structure is being 3D printed. This technique offers proprioceptive feedback with minimal hysteresis, enabling accurate pose reconstruction from these fluidic sensors alone. I close my talk with a discussion on how these methods can be generalized beyond auxetics through computational design and participatory design techniques.
Biography
Lillian Chin is a PhD candidate at MIT’s Computer Science and Artificial Intelligence Lab, working with Daniela Rus. She is interested in designing robotic bodies and their materials for optimized interaction with their environment through embedded perception and computational design. She is the recipient of several fellowships including the NSF Graduate Research Fellowship, the Hertz Foundation Graduate Fellowship and the Paul and Daisy Soros Fellowship for New Americans. Her work has been published in Science and Science Advances and has been recognized with awards such as the 2019 IEEE Robosoft Best Poster Award, the 2019 ACM CS and Law Best Paper Award and the 2022 Leventhal City Prize.
Lillian has also focused heavily in research mentorship, mentoring 21 undergraduates and 2 masters students over her PhD to write 8 papers. Nearly two thirds of these students were women and other gender minorities, nearly half were underrepresented racial minorities, and a third were co-authors on papers.
