Simon Huber

Papers

Total Citations

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About

Simon Huber’s research sits at the intersection of robotic motion planning, human-robot interaction, and computational design. His most influential work introduces **differentiable collision avoidance using collision primitives**, a novel optimization-based framework that streamlines how robots compute safe trajectories. By making collision avoidance differentiable, Huber’s approach allows gradient-based solvers to handle complex, cluttered environments more efficiently—a key advance for real-time robotic manipulation. This paper has already garnered **20 citations** since its 2022 publication, signaling strong impact in the motion planning community. Huber also explores **task autocorrection for immersive teleoperation**, where he addresses the cognitive burden of remote control by enabling the robot to autonomously correct operator errors, reducing mental load and training time. His work extends beyond traditional robotics into **animatronic design**, where he developed a globally optimal discrete search algorithm to synthesize actuation systems for expressive robotic characters. This cross-disciplinary portfolio—from foundational motion planning to applied teleoperation and creative robotics—demonstrates Huber’s ability to solve practical, high-impact problems. His contributions are shaping how robots move safely and interact intuitively with humans.