Abstract: Achieving robust mobility on natural and deformable terrains is pivotal for robots to operate effectively in real-world scenarios. Despite remarkable progress in robotics hardware and software, today’s robots still face challenges in traversing terrains like sand dunes, soft snow, and sticky mud, significantly trailing behind the locomotion abilities of animals and humans. This gap limits robots’ capabilities to aid in critical missions such as earthquake search and rescue, supply delivery, and planetary exploration.
This talk discusses our recent efforts to bridge this gap. First, we show that by understanding the force responses from deformable terrains, we could allow robots to elicit desired ground reaction forces from challenging terrains like sand and mud and produce significantly improved locomotion performance. Second, we show that by leveraging the high force transparency of direct-drive actuators, robots could use their legs as proprioceptive sensors to determine substrate strength and mechanical properties. This proprioceptive sensing capability can enable robots to gather rich information from their environment during every step, and adapt their locomotion strategies accordingly. Finally, we discuss our latest progress in applying these locomotion and sensing strategies in earth and planetary exploration scenarios, and how the improved sensing and locomotion capabilities pave the way for new human-robot teaming workflows.
Bio: Feifei Qian is an Assistant Professor of Electrical and Computer Engineering at the University of Southern California. Qian received her PhD in Electrical Engineering and M.S. in Physics from Georgia Institute of Technology, in 2015 and 2011, respectively. Prior to her appointment at USC, she worked in the GRASP lab at University of Pennsylvania as a postdoctoral fellow. Qian’s expertise is in analyzing and modeling the complex interactions between robots and environments and developing innovative control and sensing strategies to improve robot mobility on challenging terrains. Qian’s research has been recognized with NSF CAREER award, best student paper award from the Robotics Science and Systems conference and has been featured in several media press including BBC News, Phys.org, and R&D Magazine.
