Since the 1960’s we have lived with the promise of one day being able to own a robot that would be able to co-exist, collaborate and cooperate with humans in our everyday lives. This promise has motivated a vast amount of research in the last decades on motion planning, machine learning, perception and physical human-robot interaction (pHRI). Nevertheless, we are yet to see a truly collaborative robot navigating and manipulating objects, the environment or physically collaborating with humans and other robots outside of labs and in the human-centric dynamic spaces we inhabit; i.e., “in-the-wild”. This bottleneck is due to a robot-centric set of assumptions of how humans interact and adapt to technology and machines. In this talk, I will introduce a set of more realistic human-centric assumptions and I posit that for collaborative robots to be truly adopted in such dynamic, ever-changing environments they must possess human-like characteristics of reactivity, compliance, safety, efficiency and transparency. Combining these objectives is challenging as providing a single optimal solution can be intractable and even infeasible due to problem complexity and contradicting goals. Hence, I will present possible avenues to achieve these requirements. I will show that by adopting a Dynamical System (DS) based approach for motion planning we can achieve reactive, safe and provably stable robot behaviors while efficiently teaching the robot complex tasks with a handful of demonstrations. Further, I will show that such an approach can be extended to offer task-level reactivity and can be adopted to efficiently and incrementally learn from failures, as humans do. I will also discuss the role of compliance in collaborative robots, the allowance of soft impacts and the relaxation to the standard definition of safety in pHRI and how this can be achieved with DS-based and optimization-based approaches. I will then talk about the importance of both end-users and designers having a holistic understanding of their robot’s behaviors, capabilities, and limitations and present an approach that uses Bayesian posterior sampling to achieve this. The talk will end with a discussion of open challenges and future directions to achieve truly collaborative robots in-the-wild.
Bio: Nadia Figueroa is the Shalini and Rajeev Misra Presidential Assistant Professor in the Mechanical Engineering and Applied Mechanics (MEAM) Department at the University of Pennsylvania. She holds a secondary appointment in the Computer and Information Science (CIS) department and is a faculty advisor at the General Robotics, Automation, Sensing & Perception (GRASP) laboratory. Before joining the faculty, she was a Postdoctoral Associate in the Interactive Robotics Group of the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT), advised by Prof. Julie A. Shah. She completed a Ph.D. (2019) in Robotics, Control and Intelligent Systems at the Swiss Federal Institute of Technology in Lausanne (EPFL), advised by Prof. Aude Billard. Prior to this, she was a Research Assistant (2012-2013) at the Engineering Department of New York University Abu Dhabi (NYU-AD) and in the Institute of Robotics and Mechatronics (2011-2012) at the German Aerospace Center (DLR). She holds a B.Sc. degree in Mechatronics (2007) from Monterrey Tech (ITESM-Mexico) and an M.Sc. degree in Automation and Robotics (2012) from the Technical University of Dortmund, Germany. Her main research interest focuses on developing collaborative human-aware robotic systems: robots that can safely and efficiently interact with humans and other robots in the human-centric dynamic spaces we inhabit. This involves research at the intersection of machine learning, control theory, artificial intelligence, perception, and psychology - with a physical human-robot interaction perspective.