Papers
7
Total Citations
241
H-Index
5
About
Joseph McIntyre's research career bridges two seemingly distinct worlds: the precision of robotic control and the complexity of human motor coordination. His early work in the 1980s pioneered model-based robot learning, introducing algorithms that allowed robots to refine their movements through practice by using inverse models to process trajectory errors—a foundational contribution that earned over 158 citations for his 1986 paper. This work laid the groundwork for adaptive feedforward control in robotics, demonstrating how machines could learn from performance errors to improve positioning and trajectory following. More recently, McIntyre has turned his attention to understanding human movement, particularly the biomechanics and neural control of catching. His 2012 study on minimum jerk trajectories in 3D catching movements revealed how humans optimize speed and smoothness when intercepting a ball, while his work on the NEURARM platform—a bio-inspired robotic arm—models the antagonistic, non-linear actuation of human joints. By developing robotic systems that imitate human neuromuscular properties, McIntyre has created powerful tools for investigating motor control, showing how mechanical stiffness and neural oscillations may contribute to movement stability. His career exemplifies how robotics can illuminate human physiology.
Research Focus
Key Achievements
Top Papers
- 1Robot trajectory learning through practice158 citations · 1986
- 2Minimum jerk for human catching movements in 3D26 citations · 2012
- 3
- 4A robotic model to investigate human motor control16 citations · 2011
- 5Model-based robot learning16 citations · 1988
- 6Does the brain make waves to improve stability?5 citations · 2008
- 7An Application of Adaptive Feedforward Control to Robotics2 citations · 1987