Title: Integrated Learning and Planning with Neuro-Symbolic Concepts
Speaker: Jiayuan Mao
Location: Gates B3 (link)
Attendance Link: https://tinyurl.com/robosem-spr-2026
Abstract: I aim to build complete intelligent agents that can continually learn, reason, and plan: answer queries, infer human intentions, and make long-horizon plans spanning hours to days. In this talk, I will describe a general learning and reasoning framework based on neuro-symbolic concepts. Drawing inspiration from theories and studies in cognitive science, neuro-symbolic concepts serve as compositional abstractions of the physical world, representing object properties, relations, and affordances, and actions. These concepts can be combinatorially reused in flexible and novel ways. Technically, each neuro-symbolic concept is represented as a combination of symbolic programs, which define how concepts can be structurally combined (similar to the ways that words form sentences in human language), and modular neural networks, which ground concept names in sensory inputs and agent actions. I show that systems that leverage neuro-symbolic concepts demonstrate superior data efficiency, enable agents to reason and plan more quickly, and achieve strong generalization in novel situations and for novel goals.
Bio: Jiayuan Mao is a member of technical staff at Amazon Frontier AI & Robotics, and an incoming assistant professor at the University of Pennsylvania. She finished her PhD at MIT, advised by Professors Josh Tenenbaum and Leslie Kaelbling. Her research agenda is to build machines that can continually learn concepts (e.g., properties, relations, rules, and skills) from their experiences and apply them for reasoning and planning in the physical world. Her research topics include visual reasoning, robotic manipulation, scene and activity understanding, and language acquisition. She was named a Rising Star in EECS (2024) and in Generative AI (2024). Her research has received Best Paper Awards at CogSci 2024, SoCal NLP 2024, and the CoRL 2024 Workshop on Language and Robot Learning, as well as a Best Paper nomination at ACL 2019.
Please visit https://stanfordasl.github.io/robotics_seminar/ for this quarter’s lineup of speakers. Although we encourage live in-person attendance, recordings of talks will be posted also.
If you’re interested, you’re welcome to join Jiayuan for lunch at Blend Cafe at 12 PM.