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About

Mustafa Mukadam is a robotics researcher whose work sits at the intersection of motion planning, probabilistic inference, and robot perception. He is best known for pioneering the application of Gaussian processes to motion planning, demonstrated through his highly influential 2016 papers — "Gaussian Process Motion Planning" and "Motion Planning as Probabilistic Inference using Gaussian Processes and Factor Graphs" — which together have accumulated over 250 citations and reframed trajectory optimization as a problem of probabilistic inference. This foundational perspective was extended through continuous-time trajectory representations on matrix Lie groups and unified frameworks for simultaneous estimation and planning. Beyond motion planning, Mukadam has made significant contributions to robot perception and manipulation, including iSDF, a real-time neural signed distance field system for collision-aware robot perception (125 citations), and NeuralFeels, which advances visuotactile sensing for dexterous in-hand manipulation. His 2022 open-source library Theseus brought differentiable nonlinear optimization to the broader robotics and vision community. Contributing also to embodied AI through Habitat 2.0 and grasp learning via neural fields, Mukadam's research consistently bridges principled mathematical frameworks with practical robotic systems, making him a distinctive voice in modern robot learning and planning research.