Advances in machine learning have accelerated the introduction of autonomy in our everyday lives. However, ensuring that these autonomous systems act as intended is an immense challenge. Today, when self-driving vehicles or collaborative robots operate in real-world uncertain environments, it is impossible to guarantee safety at all times. A key challenge stems from the uncertainty of the environment itself, and the inability to predict all possible situations and interactions that could confront the system. Machine learning, and its potential ability to generalize, may provide a solution. For example, a learning-based perception system for a self-driving vehicle must be able to generalize beyond the scenes that it has observed in training. Similarly, learned dynamical driving policies must successfully execute agile safety maneuvers in previously unexperienced scenarios. And yet today, these learning algorithms are producing solutions that are not easy to understand and may be brittle to faults and possible cyber-attacks. In addition, machine learning-based autonomy is largely being designed in isolation from the people who would use it, rather than being built from the ground up for interaction and collaboration.
In this workshop, we explore the scope of safe autonomy, present and identify the challenges, and to explore current research developments which help us move towards a solution. It includes talks from researchers and practitioners in academia, industry, and government from diverse areas such as control and robotics, AI and machine learning, formal methods, and human-robot interaction, and their applications to the domains of ground, air, and space vehicles as well as medical robotics.
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ORGANIZERS
Geir Dullerud (University of Illinois at Urbana-Champaign), Claire Tomlin (University of California, Berkeley)
SPEAKERS
Pieter Abbeel (University of California, Berkeley), Lars Blackmore (SpaceX), JP Clarke (Georgia Institute of Technology), Anca Dragan (University of California, Berkeley), Hadas Kress-Gazit (Cornell University), Sayan Mitra (University of Illinois at Urbana-Champaign), Sandeep Neema (Defense Advanced Research Projects Agency), George Pappas (University of Pennsylvania), Daniela Rus (Massachusetts Institute of Technology), Dawn Tilbury (National Science Foundation, University of Michigan), Jur van den Berg (Ike), Keenan Wyrobek (Zipline)
