Matthew Travers

Matthew Travers is a Systems Scientist in the Mechanical Engineering department at Carnegie Mellon University. His research focuses on developing the intelligence necessary to enable complex platforms to autonomously interact with and perform meaningful work in complex environments. Current task areas 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. His work is grounded in classical control theory, Bayesian inference, practical optimal control, and modern reinforcement learning. Travers can be contacted at mtravers@andrew.cmu.edu.

Matthew Travers is a researcher at Carnegie Mellon University specializing in Robotics, biologically-inspired systems and control, and nonlinear systems. His work explores the development of autonomous robots that mimic biological locomotion, such as snake-like robots for urban search and subterranean exploration. Travers investigates methods for improving robot mobility in complex environments, including the use of modular designs, deep reinforcement learning, and kinematic gait synthesis. His research also addresses the integration of visual sensing and inertial feedback for stable locomotion and climbing in unstructured terrain. His publications highlight the intersection of robotics, soft matter, and dynamical systems, emphasizing the application of biologically inspired principles to enhance robot autonomy and adaptability.