David Hahn
Papers
4
Total Citations
258
H-Index
4
About
David Hahn is a researcher at the intersection of computational physics, simulation, and robotics, with a focus on differentiable simulation frameworks and soft robotics. His most influential contribution, the Analytically Differentiable Dynamics (ADD) solver, has garnered over 140 citations since its 2020 publication and represents a significant advance in physics-based simulation. ADD introduces a unified, differentiable framework capable of handling frictional contact for both rigid and deformable objects — a notoriously difficult problem — by elegantly mollifying contact forces to sidestep the challenges of non-smooth dynamics. This work has become an important reference for researchers in robotics, computer graphics, and machine learning. Complementing this, Hahn's Real2Sim work (2019, 66 citations) tackles the challenge of bridging physical and virtual worlds by optimizing visco-elastic material parameters in finite element simulations to faithfully replicate real-world soft object behavior. His contributions extend into tangible hardware as well, with research on low-cost expanding foam soft robots featuring embedded sensing, highlighting a rare ability to contribute across both the theoretical and experimental dimensions of robotics research. Together, his body of work positions him as a versatile and impactful contributor to modern simulation-driven robotics.