A fast yet reliable neuro-symbolic relaxation strategy to accelerate task planning.
Chen Wang
Assistant Professor
I am an Assistant Professor at the Computer Science and Engineering (CSE), University at Buffalo (UB).
At the intersection of perception, spatial reasoning, and decision-making, our long-term research goal is to endow mobile robots with human-like autonomy. This vision drives us to develop algorithms and systems that enable robots to efficiently and robustly:
Our current work focuses on establishing the scientific foundations for combining the data-efficient learning capabilities with the structured reasoning and symbolic systems. This integration aims to empower robots to perform complex tasks with robustness, adaptability, reliability, and transparency. A central paradigm we advocate is imperative learning (IL) - a unified neuro-symbolic learning framework for robot autonomy. It is designed to be self-supervised, modular, and end-to-end trainable, seamlessly combining the flexibility of data-driven models with the generalizability of structured reasoning (physical laws and logical rules).
Work Email: chenw@sairlab.org
School Email: cwx@buffalo.edu