Dr. Roberto Martin-Martin explores the intersection of robotics, computer vision, and machine learning. In his research, he integrates physical and optimal interactions with the environment as part of novel perception and learning procedures. This provides robots new versatile and robust skills to perform tasks in human unstructured environments like homes. He worked as Postdoctoral scholar at the Stanford Vision and Learning Lab with Fei-Fei Li and Silvio Savarese. Dr. Martin-Martin received his Ph.D. and master's degree from Technische Universität Berlin (TUB) working with Professor Oliver Brock, and his BSc. degree from Universidad Politécnica de Madrid.

He writes: "I’m enthusiastic about robotics: creating machines that can perceive their environment to acquire task-relevant information about it, plan a course of action towards a desired new environment configuration, and execute the plan in a safe and robust manner, even under uncertainty and noisy actuation. We know a solution is possible just by looking at humans; understanding and getting inspired by human cognition through psychology and cognitive science is part of my methodology. In my research, I integrate artificial perception, planning, and control, and view them as learning problems. I develop solutions for simple skills such as pick-and-place, navigation, or door opening, and more complex tasks like cooking, assembling furniture, or mobile manipulation problems that combine navigation and manipulation."