Shaoting Peng

University of Pennsylvania, University of Illinois Urbana-Champaign

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About

Shaoting Peng is a rising researcher at the forefront of human-robot interaction, with a focus on creating safer, more intuitive, and cognitively-aware robotic systems. His work uniquely bridges reactive planning, brain-computer interfaces, and uncertainty modeling to enable robots that can truly collaborate with humans. Peng’s major contributions include developing a reactive temporal logic-based planning and control framework that provides formal safety guarantees while allowing robots to adapt online to unforeseen changes—a critical advancement for interactive tasks. He has also pioneered the integration of object permanence filters into robotic tracking, allowing robots to maintain awareness of occluded objects, mirroring a key aspect of human cognitive development. In the domain of assistive robotics, his work on EEG-based motor intention detection demonstrates the feasibility of real-time, non-invasive control of robotic arms for individuals with motor impairments. Most recently, Peng has tackled the challenge of uncertainty in preference learning, proposing a unified probabilistic framework to help robots better adapt to ambiguous human goals. With his most-cited paper already garnering 7 citations within its first year, Peng’s innovative, multi-disciplinary approach is shaping the next generation of interactive, assistive, and autonomous robots.