Max Robotics — Global Service Robot Directory + US Certification

About

Georg von Wichert is a robotics researcher whose work spans robot manipulation planning, contact-rich control, probabilistic state estimation, and mobile robot perception. Over more than two decades, he has made sustained contributions to the fundamental challenges of enabling robots to interact intelligently with their physical environments. His most influential work addresses the complexity of robotic manipulation, particularly the multi-modal, high-dimensional planning problems that arise when robots must coordinate their own motion with grasping and object handling. His 2017 paper on sampling-based manipulation planning (50 citations) established practical approaches to this challenge, while a cluster of 2019 publications extended this framework to dynamic environments, reactive planning, and Bayesian state estimation under contact uncertainty — collectively accumulating nearly 70 citations. His 2020 work on controlling manipulation under partial observability further demonstrates his commitment to bridging theoretical planning with real-world deployment. Earlier in his career, von Wichert pioneered self-organizing visual systems for mobile robot navigation, with papers from the late 1990s and early 2000s exploring whether robots could autonomously develop perceptual representations — a forward-thinking question that anticipated modern machine learning approaches in robotics. His semantic mapping work using Markov logic networks adds a knowledge-representation dimension to his portfolio, reflecting the breadth and continuity of his research vision.