Thomas Cohn

Papers

Total Citations

H-Index

About

Thomas Cohn is a leading researcher in robot motion planning, with a focus on overcoming the fundamental challenges of high-dimensional and constrained manipulation. His work bridges the gap between sampling-based planners and trajectory optimization, tackling the "curse of dimensionality" and the problem of local minima in nonconvex spaces. Cohn’s major contributions include pioneering the use of graphs of geodesically-convex sets for non-Euclidean motion planning, a framework that enables efficient, collision-free trajectories for complex systems. His paper on this topic, published in 2023, has already garnered 16 citations, signaling its rapid impact on the field. In 2024, Cohn advanced bimanual manipulation by developing a method that leverages analytic inverse kinematics to handle the complicated nonlinear equality constraints inherent in dual-arm object handling—a problem critical for tasks like assembly and human-robot collaboration. This work, with 11 citations, demonstrates his ability to solve real-world robotics challenges. Cohn’s research is notable for its theoretical rigor and practical applicability, making him a rising figure in robotics whose algorithms are shaping the future of autonomous manipulation.