Max Robotics — Global Service Robot Directory + US Certification

About

Max Yang is an emerging robotics researcher whose work sits at the intersection of tactile sensing, reinforcement learning, and dexterous robotic manipulation. His research addresses one of the field's most persistent challenges: enabling robots to interact with objects with the sensitivity and adaptability of human hands. Yang's most influential contribution, "Bi-Touch" (2023, 34 citations), pioneered bimanual tactile manipulation using sim-to-real deep reinforcement learning — a largely unexplored frontier that pushes toward human-level robot dexterity. Complementing this, his work on tactile pushing (2023, 18 citations) demonstrated how incorporating touch sensing dramatically improves non-prehensile manipulation beyond what vision alone can achieve. His hardware innovations are equally notable; "DexiTac" (2024, 19 citations) introduced a reconfigurable pneumatic gripper capable of handling objects of diverse shapes and sizes. Yang has also advanced the algorithmic foundations of robotic touch through graph neural network-based tactile servoing and multi-modal pushing strategies combining vision, touch, and proprioception. His 2025 review of vision-based tactile sensors further reflects a growing role as a synthesizer of the field. With over 89 cumulative citations across six papers, Yang represents a distinctive voice shaping the future of intelligent robotic manipulation.