Max Robotics — Global Service Robot Directory + US Certification

About

Greg Turk is a pioneering researcher whose work spans robotics, simulation, and computer graphics, with particular expertise in reinforcement learning, sim-to-real transfer, and robot-assisted activities of daily living. He is perhaps best known for his foundational contributions to policy learning under uncertainty, exemplified by his influential work on Universal Policies with Online System Identification (2017, 219+ citations), which demonstrated how robots can be trained in simulation to operate robustly under unknown real-world dynamics — a challenge central to modern robotics. Turk has made substantial contributions to robot-assisted dressing, a socially impactful domain addressing the needs of people with disabilities. His research tackles this problem from multiple angles: haptic perception (2016), simulation-based training (2017), deep reinforcement learning for garment manipulation (2018), and collaborative human-robot planning (2019). His 2022 review of robot learning from randomized simulations reflects his authority in this rapidly growing field. Remarkably, Turk's intellectual range extends beyond robotics — his computational study of plesiosaur locomotion (2015, 41 citations) demonstrates a creative application of simulation to paleontology. With hundreds of citations across disciplines, Turk's work exemplifies how physics-based simulation and machine learning can solve complex, real-world problems with genuine human benefit.