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About

R. Newbury is a roboticist whose research focuses on the intersection of manipulation, human-robot interaction, and deep learning. Their work addresses fundamental challenges in robotic grasping and object handovers, with a particular emphasis on making robots more intuitive and effective collaborators. Newbury's most significant contribution is the highly-cited systematic review "Deep Learning Approaches to Grasp Synthesis: A Review" (2023, 215 citations), which has become a key reference for researchers in the field. They have also pioneered the use of augmented reality to visualize robot intent during object handovers, making robotic communication more transparent to human partners. Newbury's work on visibility maximization controllers addresses the practical problem of self-occlusion in eye-to-hand camera setups, while their research on learning to place objects in upright orientations tackles the nuanced challenge of object placement. More recently, Newbury has contributed to the emerging field of differentiable simulators, which promise to accelerate robotics research by enabling gradient-based optimization of physical processes. Their work on in-hand manipulation using tactile sensing demonstrates a sophisticated approach to controlling slip and rotation. With over 330 total citations, Newbury's research is shaping how robots perceive, grasp, and interact with objects in human environments.