Max Robotics — Global Service Robot Directory + US Certification

About

Ziwei Liao is a robotics researcher whose work sits at the intersection of simultaneous localization and mapping (SLAM), semantic scene understanding, and autonomous robot navigation. His research is primarily focused on enabling mobile and service robots to perceive, map, and interact with indoor environments at the object level — moving beyond traditional geometric features to build richer, semantically meaningful representations of the world. Liao's most significant contribution is in the domain of Object SLAM, where he has advanced the use of quadric representations and semantic constraints to model objects as landmarks. His 2022 paper, "SO-SLAM," which has garnered 83 citations, introduced scale proportional and symmetrical texture constraints to tackle longstanding challenges such as partial observations and occlusions. His earlier RGB-D-based object SLAM work and object-oriented SLAM frameworks further established his expertise in building compact, accurate object-level maps. Complementing this, his research on semantic grid mapping and socially-aware robot navigation demonstrates a commitment to practical, human-friendly robotic systems. More recently, Liao has extended his focus to uncertainty-aware 3D object mapping using deep shape priors, reflecting a growing interest in robust reconstruction without relying on known CAD models. With over 170 cumulative citations, his body of work represents a meaningful and growing contribution to intelligent robotics research.