In this interview, Audrow speaks with Andrea Bajcsy and Dylan Losey about a method that allows robots to infer a human's objective through physical interaction. They discuss their approach, the challenges of learning complex tasks, and on their experience collaborating between different universities. Some examples of people working with the more typical impedance control (left) and Bajcsy and Losey's learning method (right).

Humans physically interact with each other every day – from grabbing someone's hand when they are about to spill their drink, to giving your friend a nudge to steer them in the right direction, physical interaction is an intuitive way to convey information about personal preferences and how to perform a task correctly. So why aren't we physically interacting with current robots the way we do with each other? Seamless physical interaction between a human and a robot requires a lot: lightweight robot designs, reliable torque or force sensors, safe and reactive control schemes, the ability to predict the intentions of human collaborators, and more! Luckily, robotics has made many advances in the design of personal robots specifically developed with humans in mind. However, consider the example from the beginning where you grab your friend's hand as they are about to spill their drink.