Papers
18
Total Citations
470
H-Index
11
About
Andrew S. Morgan is a robotics researcher whose work sits at the intersection of dexterous manipulation, compliant robotic hands, and autonomous planning. His research addresses some of the most persistent challenges in robotic manipulation — enabling machines to handle objects with the finesse and adaptability of human hands. Morgan has made significant contributions to in-hand manipulation, developing frameworks for finger gaiting and whole-hand contact strategies that move beyond traditional fingertip-only approaches, as demonstrated in his highly cited 2022 work on compliance-enabled manipulation (72 citations). His investigations into soft and underactuated robotic hands have produced practical solutions for everyday tasks, including pre-grasp sliding of thin objects (61 citations) and reliable high-precision assembly using vision-driven compliant control (59 citations). Morgan has also advanced data-driven approaches, applying learned state transition models and reinforcement learning to overcome the limitations of analytical modeling for complex hands. His benchmarking contributions, including adapting the Box and Blocks Test for robotic evaluation, reflect a commitment to rigorous, standardized assessment. Collectively, his body of work — spanning over 400 citations — has meaningfully advanced the field's understanding of contact-rich, dexterous robotic manipulation.
Research Focus
Key Achievements
Top Papers
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- 6Learning a State Transition Model of an Underactuated Adaptive Hand36 citations · 2019
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- 9Object-Agnostic Dexterous Manipulation of Partially Constrained Trajectories18 citations · 2020
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