Phillips Mike
Papers
1
Total Citations
15
H-Index
1
About
Mike Phillips is a leading researcher in robot manipulation and motion planning, with a focus on enabling robots to perform complex, constrained tasks in real-world environments. His major contributions center on integrating learning from demonstration with planning algorithms to tackle high-dimensional state spaces, particularly for mobile manipulation. His most cited work, "Learning to Plan for Constrained Manipulation from Demonstrations" (2013, 15 citations), addresses the challenge of opening articulated objects like doors and drawers—a notoriously difficult problem due to the expense of sampling states on constrained manifolds. By combining demonstration data with efficient planning, Phillips showed how robots can generalize from a few examples to reliably execute tasks that require precise interaction with the environment. His research has practical implications for service robotics, manufacturing, and assistive technologies, where robots must adapt to dynamic settings. Phillips’ work stands out for bridging the gap between theoretical motion planning and deployable robotic systems, making him a key figure in advancing autonomous manipulation.
Research Focus
Key Achievements
Top Papers
- 1Learning to Plan for Constrained Manipulation from Demonstrations15 citations · 2013