The digitization of practically everything coupled with advances in machine learning, the automation of knowledge work, and advanced robotics promises a future with democratized use of machines and wide-spread use of AI, robots and customization. While the last 60 years have defined the field of industrial robots, and empowered hard bodied robots to execute complex assembly tasks in constrained industrial settings, the next 60 years could be ushering in our time with Pervasive robots that come in a diversity of forms and materials, helping people with physical and cognitive tasks. However, the pervasive use of machines remains a hard problem. How can we accelerate the creation of machines customized to specific tasks? Where are the gaps that we need to address in order to advance the bodies and brains of machines? How can we develop scalable and trustworthy reasoning engines?
In this talk I will discuss recent developments in machine learning and robotics, focusing on about how computation can play a role in (1) developing Neural Circuit Policies, an efficient approach to more interpretable machine learning engines, (2) making machines more capable of reasoning in the world, (3) making custom robots, and (4) making more intuitive interfaces between robots and people.