Date: February 13, 2026
Speaker: Shiqi Zhang
Title: Robot Planning with Foundation Models: From Service to Assistive Tasks
Abstract: Robots rely on task planning to sequence high-level actions and on motion planning to generate continuous trajectories that realize those actions. Task and motion planning (TAMP) integrates these two levels of reasoning to ensure both goal completion and motion feasibility. However, deploying TAMP in the real world remains challenging: environments are open, dynamic, and full of unforeseen objects and situations. In this talk, I will present our recent work on leveraging foundation models (such as GPT and Gemini) to advance human-robot TAMP systems. I will highlight applications ranging from service robots that set tables and deliver objects, to quadruped robots that assist people with visual impairments in navigation.
Bio: Dr. Shiqi Zhang is an Associate Professor with the School of Computing, the State University of New York at Binghamton (SUNY Binghamton). His research interests include robot decision making, robot learning and human-robot systems. He was a Postdoc under Peter Stone at the University of Texas at Austin, and received his Ph.D. under Mohan Sridharan in Computer Science from Texas Tech University. He was the PI of a National Science Foundation (NSF) National Robotics Initiative project on robot decision making and is leading another NSF project on assistive robotics. He received the Best Robotics Paper Award from the 2018 AAMAS conference, a Ford URP Award in 2019, an OPPO Faculty Research Award in 2020, and an Outstanding Associate Editor recognition in 2024 from the IEEE Robotics and Automation Letters journal.