Randall T. Fawcett
Papers
12
Total Citations
175
H-Index
8
About
Randall T. Fawcett is a robotics researcher specializing in motion planning, nonlinear control, and multi-agent coordination for legged robotic systems, with a particular focus on quadrupedal locomotion. His work bridges the gap between theoretical control frameworks and real-world robotic implementation, advancing the field through innovative combinations of reduced-order modeling, data-driven techniques, and optimization-based control. Fawcett's most influential contributions include developing robust predictive control strategies that leverage learning to reconcile reduced- and full-order dynamic models for quadrupedal robots (39 citations), and pioneering layered control architectures for cooperative locomotion of multiple holonomically constrained quadrupeds (28 citations). His data-driven template modeling approach (26 citations) has offered a computationally tractable alternative to classical physics-based planners, while his QP-based virtual constraint controllers (21 citations) provide formal stability guarantees for periodic gaits. Notably, he has extended these frameworks to multi-robot systems, developing distributed predictive and data-driven planners for collaborative legged locomotion. Additional contributions include bio-inspired tail-assisted locomotion and adaptive control for payload transportation. With over 160 cumulative citations across a concise publication record spanning just a few years, Fawcett has rapidly established himself as a significant voice in the legged robotics community.
Research Focus
Key Achievements
Top Papers
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- 3Toward a Data-Driven Template Model for Quadrupedal Locomotion26 citations · 2022
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