Max Robotics — Global Service Robot Directory + US Certification

About

Bryan Gardiner’s research lies at the intersection of robotics, machine learning, and human-robot interaction, with a focus on making robots more intuitive to teach and control. His work on linguistic decision making for robot route learning (17 citations) pioneered the use of linguistic decision trees to create transparent, human-readable mappings for robot navigation, allowing users to understand and refine robot behavior through natural language. He further advanced robot programming by demonstration, developing methods for automatic code generation from task demonstrations in dynamic environments. Gardiner also contributed to accessible robotic control through adaptive gesture recognition using low-cost surface EMG sensors, democratizing hands-free robot operation. Most recently, his work on visuo-tactile object recognition employs transformers with feature-level fusion to integrate visual and tactile data—a challenging problem due to their differing statistical properties. This pipeline promises more robust robot perception for manipulation tasks. Gardiner’s research consistently bridges the gap between complex machine learning models and practical, user-friendly robotic systems, with applications ranging from assistive robotics to industrial automation.