C. J. Gross
Papers
1
Total Citations
2
H-Index
1
About
C. J. Gross is a robotics researcher whose work lies at the intersection of motion planning, machine learning, and computational geometry. Their primary research focuses on developing intelligent, data-driven methods for constructing robot configuration spaces—the fundamental maps that define which robot poses are collision-free or obstructed. In their highly innovative 2023 paper, "Direct Robot Configuration Space Construction using Convolutional Encoder-Decoders," Gross pioneered a deep learning approach that directly learns these complex spatial representations from workspace data, bypassing traditional, computationally expensive geometric calculations. This contribution is critical for enabling real-time, safe motion planning in dynamic environments. While early in their career, with this work already garnering citations, Gross is recognized for bringing modern convolutional architectures to a classic robotics problem, offering a scalable path toward more autonomous and adaptive robots. Their research promises to accelerate progress in areas from manufacturing to assistive robotics, marking them as an emerging leader in the integration of perception and planning.
Research Focus
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Top Papers
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