Max Robotics — Global Service Robot Directory + US Certification

About

Max Simchowitz is a robotics and control researcher whose work spans motion planning, contact-rich manipulation, and learning-based control. He is best known for his contributions to geometric and optimization-based approaches to robot motion planning, particularly in high-dimensional and constrained settings. His 2023 paper on Non-Euclidean motion planning with graphs of geodesically-convex sets introduced a principled framework for computing optimal, collision-free trajectories that overcomes the local minima pitfalls common to trajectory optimizers — work that has already garnered 16 citations. Building on this foundation, his research on constrained bimanual planning with analytic inverse kinematics (11 citations) addresses the intricate nonlinear constraints arising when two robot arms jointly manipulate an object. Simchowitz has also investigated the fundamental challenges of contact-rich manipulation, examining whether linear feedback on smoothed dynamics is sufficient for stabilization — a question with significant practical implications. Earlier work in adaptive robotic sensing and statistical learning theory for linear control reflects his broader interest in bridging machine learning and control systems. Across these contributions, Simchowitz has established himself as a researcher working at the intersection of theoretical rigor and practical robotics, offering tools that meaningfully advance autonomous manipulation.