Collaborative Robotics: From Dexterity to Teammate Prediction


Computer Science Department, Stanford University


John Leonard

There have been amazing advances in the field of robotic autonomy over the past few decades, we’ve seen robots move from factory floors to working examples of autonomous vehicles on public roads. However, there is still a notable barrier to having robots in everyday environments performing functional tasks. These barriers are usually due to current robots having limited dexterity when it comes to manipulating objects that humans can handle with minimal difficulty, to barriers that exist when a robot is supposed to work alongside a human to complete a task or enable a human to perform a task better. In this talk, we will address the catalyst problem of robotic autonomy in human environments by addressing robotic dexterity and the necessity of improving the sense of robotic touch. We will then look at how robots can become effective teammates by modeling the behavior of their collaborators in the context of the task or environment, which enables them to predict future behavior and take collaborative actions.

Biography: Monroe Kennedy is an assistant professor in Mechanical Engineering and by courtesy, Computer Science at Stanford University. Monroe is the recipient of the NSF Faculty Early Career Award. He directs the Assistive Robotics and Manipulation Laboratory (ARMLab), where the focus is on developing collaborative, autonomous robots capable of performing dexterous, complex tasks with human and robotic teammates. Monroe received his Ph.D. in Mechanical Engineering and Applied Mechanics and master’s in Robotics from the University of Pennsylvania.