Matthew Travers’ research focuses on developing the intelligence necessary to enable complex platforms to autonomously interact with and perform meaningful work in complex environments. The task areas on which he is currently focusing include biologically inspired dynamic locomotion and learning, compliant manipulation for agriculture and food preparation, managing uncertainty in human-robot interaction, and field-ready search and rescue robotics. The central ideas that underlie the analytical aspects of Travers’ work are drawn from classical control theory, Bayesian inference, practical optimal control, and modern reinforcement learning.