Early Career Spotlights

Title: Robot Programming for All

Abstract: Robots that can assist humans in everyday tasks have the potential to improve people’s quality of life and bring independence to persons with disabilities. A key challenge in realizing such robots is programming them to meet the unique and changing needs of users and to robustly function in their unique environments. Most research in robotics targets this challenge by attempting to develop universal or adaptive robotic capabilities. This approach has had limited success because it is extremely difficult to anticipate all possible scenarios and use-cases for general-purpose robots or collect massive amounts of data that represent each scenario and use-case. Instead, my research aims to develop robots that can be programmed in-context and by end-users after they are deployed, tailoring it for the specific environment and user preferences. To that end, my students and I have been developing new techniques and tools that allow intuitive and rapid programming of robots to do useful tasks. In this talk I will introduce some of these techniques and tools, demonstrate their capabilities, and discuss some of the challenges in making them work in the hands of potential users and deploy them in the real world.

Biography: Maya Cakmak is an Assistant Professor at the University of Washington, Computer Science & Engineering Department, where she directs the Human-Centered Robotics lab. She received her PhD in Robotics from the Georgia Institute of Technology in 2012, after which she spent a year as a post-doctoral research fellow at Willow Garage, one of the most influential robotics companies. Her research interests are in human-robot interaction, end-user programming, and assistive robotics. Her work aims to develop robots that can be programmed and controlled by a diverse group of users with unique needs and preferences to do useful tasks. Maya's work has been published at major Robotics and AI conferences and journals, demonstrated live in various venues and has been featured in numerous media outlets. Tools that she and her students developed are currently being used by robotics companies like Savioke and Fetch Robotics. She received an NSF CAREER award in 2016 and a Sloan Research Fellowship in 2018.

Title: Learning Models of Language, Action and Perception for Human-Robot Collaboration

Abstract: Robots can act as a force multiplier for people, whether a robot assisting an astronaut with a repair on the International Space station, a UAV taking flight over our cities, or an autonomous vehicle driving through our streets. To achieve complex tasks, it is essential for robots to move beyond merely interacting with people and toward collaboration, so that one person can easily and flexibly work with many autonomous robots. The aim of my research program is to create autonomous robots that collaborate with people to meet their needs by learning decision-theoretic models for communication, action, and perception. Communication for collaboration requires models of language that map between sentences and aspects of the external world. My work enables a robot to learn compositional models for word meanings that allow a robot to explicitly reason and communicate about its own uncertainty, increasing the speed and accuracy of human-robot communication. Action for collaboration requires models that match how people think and talk, because people communicate about all aspects of a robot's behavior, from low-level motion preferences (e.g., "Please fly up a few feet") to high-level requests (e.g., "Please inspect the building"). I am creating new methods for learning how to plan in very large, uncertain state-action spaces by using hierarchical abstraction. Perception for collaboration requires the robot to detect, localize, and manipulate the objects in its environment that are most important to its human collaborator. I am creating new methods for autonomously acquiring perceptual models in situ so the robot can perceive the objects most relevant to the human's goals. My unified decision-theoretic framework supports data-driven training and robust, feedback-driven human-robot collaboration.

Biography: Stefanie Tellex is the Joukowsky Family Assistant Professor of Computer Science and Assistant Professor of Engineering at Brown University. Her group, the Humans To Robots Lab, creates robots that seamlessly collaborate with people to meet their needs using language, gesture, and probabilistic inference, aiming to empower every person with a collaborative robot. She completed her Ph.D. at the MIT Media Lab in 2010, where she developed models for the meanings of spatial prepositions and motion verbs. Her postdoctoral work at MIT CSAIL focused on creating robots that understand natural language. She has published at SIGIR, HRI, RSS, AAAI, IROS, ICAPs and ICMI, winning Best Student Paper at SIGIR and ICMI, Best Paper at RSS, and an award from the CCC Blue Sky Ideas Initiative. Her awards include being named one of IEEE Spectrum's AI's 10 to Watch in 2013, the Richard B. Salomon Faculty Research Award at Brown University, a DARPA Young Faculty Award in 2015, a NASA Early Career Award in 2016, a 2016 Sloan Research Fellowship, and an NSF Career Award in 2017. Her work has been featured in the press on National Public Radio, BBC, MIT Technology Review, Wired and Wired UK, as well as the New Yorker. She was named one of Wired UK's Women Who Changed Science In 2015 and listed as one of MIT Technology Review's Ten Breakthrough Technologies in 2016.

Title: Robots that learn and improve through experience

Abstract: Advances in machine learning have made it possible to build algorithms that can make complex and accurate inferences for open-world perception problems, such as recognizing objects in images or recognizing words in human speech. These advances have been enabled by improvements in models and algorithms, such as deep neural networks, advances in the amount of available computation and, crucially, the availability of large amounts of manually-labeled data. Robotics presents a major challenge for these technologies, but also a major opportunity: robots can interact autonomously with the world, which can allow them to learn behavioral skills and a data-driven understanding of physical phenomena with minimal human supervision. This can in principle provide us with a scalable mechanism by which robots can acquire large repertoires of generalizable skills, bringing us closer to general-purpose robotic systems that can fulfill a wide range of tasks and goals. In this talk, I will discuss how autonomous robotic learning can give rise to behavioral skills that generalize effectively without detailed human supervision, as well as the major challenges in our current approaches to this problem and perspectives on future directions.

Biography: Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business.