Early Career Awards

Five Traps for Robots in Human Environments....And How to Avoid Them

Robotics today is moving beyond fixed environments and into human spaces like homes, restaurants, and hospitals. In these new spaces, robots will necessarily have to interact with people. In some sense, every recent robotics problem is partly a human-robot interaction problem. Thus, the field of HRI can offer insights to the broader robotics community about how to create effective and beneficial robot systems that interact well with people. In this talk, I cover five common assumptions about humans and robots, explain why they fail, and describe how we address them through examples from my own work.

Henny Admoni is the A. Nico Habermann Assistant Professor in the Robotics Institute at Carnegie Mellon University, where she leads the Human And Robot Partners (HARP) Lab. Dr. Admoni studies how to develop intelligent robots that can assist and collaborate with humans on complex tasks like preparing a meal. She is most interested in how natural human communication, like where someone is looking, can reveal underlying human intentions and can be used to improve human-robot interactions.
Dr. Admoni has been awarded an NSF CAREER grant, an Okawa Research Grant, and the A. Nico Habermann Career Development Professorship at CMU. Dr. Admoni’s research has been further supported by the US National Science Foundation, the US Office of Naval Research, the Paralyzed Veterans of America Foundation, and Sony Corporation. Her work has been featured by media such as NPR’s Science Friday, Voice of America News, and WESA radio.
Previously, Dr. Admoni was a postdoctoral fellow at CMU with Siddhartha Srinivasa in the Personal Robotics Lab. She completed her PhD in Computer Science at Yale University with Professor Brian Scassellati in the Social Robotics Lab. Her PhD dissertation was about modeling the complex dynamics of nonverbal behavior for socially assistive human-robot interaction. Dr. Admoni also holds an MS in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University.

Teaching Robots Through Interactions

The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. A human teacher is always involved in the learning process - either directly (providing data) or indirectly (designing the optimization criterion) - which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective? In this talk I’ll argue that there are tremendous benefits in having a human teacher intermittently interact with a robot also while it is learning. I will present some of the interactive learning methods we have been developing and demonstrate their capabilities with tasks ranging from fun (ball-in-a-cup) to more applied (retail environments).

Jens Kober is an associate professor at TU Delft, Netherlands. He is member of the Cognitive Robotics department (CoR), the TU Delft Robotics Institute, and RoboValley.
Jens is the recipient of the IEEE-RAS Early Academic Career Award in Robotics and Automation 2018. His Ph.D. thesis has won the 2013 Georges Giralt PhD Award as the best Robotics PhD thesis in Europe in 2012. Jens was an assistant professor at TU Delft (2015-2019), first at the Delft Center for Systems and Control (DCSC) and later at CoR. He worked as a postdoctoral scholar (2012-2014) jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany.
From 2007-2012 he was working with Jan Peters as a master's student and subsequently as a Ph.D. student at the Robot Learning Lab, Max-Planck Institute for Intelligent Systems, Empirical Inference Department (formerly part of the MPI for Biological Cybernetics) and Autonomous Motion Department. Jens has graduated in Spring 2012 with a Doctor of Engineering “summa cum laude” from the Intelligent Autonomous Systems Group, Technische Universität Darmstadt.