Papers
6
Total Citations
218
H-Index
6
About
David J. Bennett’s research bridges two transformative fields: autonomous robot calibration and neurorehabilitation robotics. His early work established foundational methods for self-calibrating manipulators using only internal sensors, enabling robots to autonomously determine their kinematic models—a breakthrough documented in his 1991 paper (98 citations) and refined through closed-loop identification techniques (2003, 19 citations). This work remains influential in robotics for its elegant solution to hand-eye coordination without external metrology. More recently, Bennett has pioneered automated rehabilitation robots for rodent models of spinal cord injury. His team developed the first fully automated, ad libitum training system for single-pellet grasping tasks, enabling 24/7 rehabilitation without human intervention. This innovation, detailed in papers from 2014 (31 citations) and 2015 (28 citations), led to the discovery of critical intensity thresholds for forelimb recovery after cervical injury (2020, 34 citations). By combining rigorous kinematic analysis with behavioral neuroscience, Bennett has created a powerful platform for understanding neural plasticity and optimizing rehabilitation protocols. His work exemplifies how robotics can both advance fundamental science and translate into clinical therapies for paralysis.
Research Focus
Key Achievements
Top Papers
- 1Autonomous Robot Calibration for Hand-Eye Coordination98 citations · 1991
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- 5Identifying the kinematics of robots and their tasks19 citations · 2003
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