Robert Peter Matthew

University of California, Berkeley, Imperial College London, University of California, San Francisco

Papers

Total Citations

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About

Robert Peter Matthew is a robotics researcher whose work spans haptic perception, bio-inspired design, constraint inference, and assistive exoskeletons. His most cited paper, "Haptic Perception of Liquids Enclosed in Containers" (2019, 22 citations), addresses a critical gap in robotic manipulation: sensing liquids obscured by opaque containers. By focusing on haptic rather than visual cues, Matthew advanced the ability of service robots to perform precise pouring tasks—a key skill for real-world applications. Earlier, he contributed to space robotics with a bio-inspired compliant claw for arboreal locomotion in microgravity (2010), offering a novel solution for robots navigating small celestial bodies or spacecraft. In "Maximum Likelihood Constraint Inference on Continuous State Spaces" (2022), Matthew developed a method for robots to infer constraints from continuous, sub-optimal demonstrations, enhancing safety in human-robot interaction. His work on an active/passive exoskeleton (2015) introduced a hybrid device that overcomes the limitations of purely passive systems, improving assistive performance during tasks like hammer curls. With contributions spanning manipulation, space exploration, and rehabilitation, Matthew’s research demonstrates a commitment to practical, interdisciplinary robotics.