Papers
7
Total Citations
58
H-Index
4
About
Ryan Carpenter’s research lies at the intersection of robotic grasping, machine learning, and soft actuation, with a focus on making robotic manipulation more adaptive, robust, and human-like. His most influential work, “Evaluating the efficacy of grasp metrics for utilization in a Gaussian Process-based grasp predictor” (21 citations), introduced a novel machine learning framework that predicts grasp quality before execution, validated on a physical platform. This approach was further refined in his follow-up paper on Gaussian process-based grasp prediction (13 citations). Carpenter also advanced underactuated gripper design, proposing passive hydraulic mechanisms for industrial robots to improve disturbance rejection while keeping costs low (9 citations). His more recent contributions explore Synthetic Muscle electroactive polymers (EAPs) for prosthetic and robotic applications, demonstrating low-voltage actuation and pressure sensing that mimics human tissue—work that has been recognized for its potential to reduce prosthetic slippage and tissue breakdown. With over 50 total citations across his publications, Carpenter’s work bridges simulation, crowdsourced data generation, and real-world industrial deployment, making him a notable figure in the push toward more intelligent and dexterous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Implementation of a Gaussian process-based machine learning grasp predictor13 citations · 2015
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