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About

Simon Zimmermann is a roboticist whose research sits at the intersection of manipulation, motion planning, and legged locomotion. He is best known for pioneering work that pushes the boundaries of what mobile robots can physically achieve. His most impactful contribution, "Go Fetch! - Dynamic Grasps using Boston Dynamics Spot with External Robotic Arm" (106 citations), demonstrated how to transform a standard quadruped into a dynamic grasping platform, enabling it to pick up objects while in motion—a feat that has captured the imagination of the robotics community. Zimmermann’s work is characterized by a rigorous optimization-based approach. His "Multi-Level Optimization Framework for Simultaneous Grasping and Motion Planning" (42 citations) elegantly solves the chicken-and-egg problem of deciding where to grasp while planning a trajectory. He has also brought artistry to robotics with "PuppetMaster" (32 citations), a computational framework that allows robots to animate real-world string puppets. Beyond these highlights, Zimmermann has advanced the manipulation of deformable objects, developed differentiable collision avoidance techniques, and used motion matching to generate lifelike animal gaits on quadrupedal robots. His recent work on "Gradient-Based Trajectory Optimization With Learned Dynamics" (2023) signals a move toward combining model-based control with data-driven methods, promising even more adaptive and capable robots in the future.