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About

Matthew Klingensmith is a leading researcher in robotics, with a focus on motion planning, autonomous manipulation, and state estimation under uncertainty. His most influential contribution is CHOMP (Covariant Hamiltonian Optimization for Motion Planning), a seminal method for trajectory optimization that uses functional gradient techniques to iteratively refine trajectories. With 738 citations, CHOMP has become a foundational tool in the field, prized for its invariance to reparametrization and its ability to trade off between path smoothness and obstacle avoidance. Klingensmith also developed an integrated system for autonomous robotics manipulation, which tightly couples perception, planning, and control to enable dexterous grasping with minimal supervision. His work on touch-based localization leverages submodularity to efficiently gather information, while ARM-SLAM addresses the challenge of simultaneous localization and mapping when a robot’s joint angles are uncertain. More recently, he introduced the Manifold Particle Filter, a novel approach for state estimation on high-dimensional implicit manifolds, informed by contact sensors. Klingensmith’s research consistently pushes the boundaries of how robots perceive, plan, and interact with the physical world.