Henny Admoni
Assistant Professor | Carnegie Mellon University
Pittsburgh, PA, UNITED STATES
Henny Admoni directs the Human And Robot Partners (HARP) Lab, which develops assistive and collaborative robots and AI.
Biography
Henny Admoni is an Assistant Professor in the Robotics Institute at Carnegie Mellon University, and also has a courtesy appointment in the Human-Computer Interaction Institute at CMU. She leads the Human And Robot Partners (HARP) Lab, which studies how to develop intelligent robots that can assist and collaborate with humans on complex tasks like preparing a meal. Henny 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. Henny's research has been supported by federal agencies and industry, such as 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 the media such as NPR's Science Friday, Voice of America News, and WESA radio. Previously, Henny was a postdoctoral fellow at CMU. She holds an MS and PhD in Computer Science from Yale University, and a BA/MA joint degree in Computer Science from Wesleyan University.
Areas of Expertise (6)
Cognitive Psychology
Robotics
Human-Robot Interaction (HRI)
Healthcare
Human Assistance
Computer Science
Media Appearances (5)
Robotics Professor Answers Robot Questions From Twitter
Wired Online online
2022-11-22
Robotics professor Henny Admoni answers the internet's burning questions about robots! How do you program a personality? Can robots pick up a single M&M? Why do we keep making humanoid robots? What is Elon Musk's goal for the Tesla Optimus robot? Will robots take over my job writing video descriptions...I mean, um, all our jobs? Henny answers all these questions and much more.
Supporting Black Scholars in Robotics
IEEE Spectrum online
2020-09-10
Robotics is a fast-growing field with important economic and societal impacts. Despite the relevance of robotics, however, there is little diversity among educators and researchers in the area. This problem is especially acute among Black scholars and is not improving. In this article, we outline the representation problem and introduce a reading list along with suggestions for how those in academia—researchers, teachers, students, conference organizers, and others—can take actions that increase Black representation in robotics. While our analysis focuses on the situation in the United States, we hope that our suggestions will be of use to colleagues in other countries as well.
Boston Dynamics’ Spot is leaving the laboratory
The Verge online
2019-09-24
That’s the opposite of what many academic roboticists focus on, and Henny Admoni, who works on Human-Robot interaction at Carnegie Mellon University, told me it was an understandable but tricky trade-off. “Boston Dynamics has always been strong in mechanics and controls, like being able to shift the robot’s weight properly,” Admoni told me. “But robots operating in human environments won’t really have the option of avoiding humans. Integrating Human-Robot Interaction skills into development at an early stage is probably going to lead to greater success than trying to retrofit human interaction into existing systems.”
Amazon jumps into the home robot race
Axios online
2018-04-23
Why it matters: Amazon is stepping into a newish, already existing market for home, socially interactive robots, says Henny Admoni, a robotics professor at Carnegie Mellon. "A lot of people are excited about them, but I wouldn't say Amazon is the first," Admoni told Axios.
Let Robots Teach Our Kids? Here's Why That Isn't Such a Bad Idea
NBC News online
2017-04-19
Despite these clever androids, we're still far from having Rosie-like robots we can trust as nannies. Today's autonomous bots still lack the manual dexterity to, say, pour a child a cup of milk, or the emotional instincts to soothe a crying toddler. "Having a robot that's capable of those types of things won't come for the next 20 or 30 years," says Henny Admoni, a roboticist at Carnegie Mellon University.
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Industry Expertise (2)
Research
Education/Learning
Accomplishments (5)
RSS Early Career Spotlight (professional)
2022
A. Nico Habermann Career Development Professorship (professional)
2020–2023
CMU Distinguished Lecture: Teruko Yata Memorial Lecture in Robotics (professional)
2019
RoboHub 50 Women in Robotics You Need to Know About (professional)
2021
NSF CAREER Award (professional)
2020
Education (4)
Yale University: Ph.D., Computer Science 2016
Yale University: M.S., Computer Science 2012
Wesleyan University: M.A., Computer Science 2009
Wesleyan University: B.A., Computational Cognitive Science 2008
Affiliations (5)
- IEEE-RAS Women in Engineering Committee
- Black In Robotics Allies : Steering Committee
- Robotics Institute Climate Committee : Co-chair
- Robotics Institute Education Committee
- Transactions on Human-Robot Interaction : Associate Editor
Links (5)
Event Appearances (5)
Human Interactive Robot Learning Workshop
HRI 2023
Cognitive Modeling in Robot Learning for Adaptive HRI Workshop
ICRA 2023
Communicating Robot Learning Across Human-Robot Interaction Workshop
ICRA 2023
Open Source Benchmarking for Robotics Workshop
HRI 2023
Early Career Spotlight Lecture
RSS 2022 New York, NY
Articles (5)
Towards Online Adaptation for Autonomous Household Assistants
HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction2023 Many assistive home robotics applications assume open-loop interactions: robots incorporate little feedback from people while autonomously completing tasks. This places undue burden on people to condition their actions and environment to maximize the likelihood of their desired outcomes. We formalize assistive household rearrangement as collaborative online inverse reinforcement learning (IRL).
Teaching agents to understand teamwork: Evaluating and predicting collective intelligence as a latent variable via Hidden Markov Models
Computers in Human Behavior2023 Rapid growth in the reliance on teamwork in organizations, coupled with advances in artificial intelligence, has fueled increased use of Human Autonomy Teams (HATs) involving the collaboration of humans and agents to complete work. Although there are many successful examples of HATs, researchers and technology developers can see additional applications if agents were better able to understand the mental states of humans to anticipate what a team is likely to do next.
The Role of Adaptation in Collective Human–AI Teaming
Topics in Cognitive Science2022 This paper explores a framework for defining artificial intelligence (AI) that adapts to individuals within a group, and discusses the technical challenges for collaborative AI systems that must work with different human partners. Collaborative AI is not one-size-fits-all, and thus AI systems must tune their output based on each human partner's needs and abilities. For example, when communicating with a partner, an AI should consider how prepared their partner is to receive and correctly interpret the information they are receiving.
Coordination With Humans Via Strategy Matching
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2022 Human and robot partners increasingly need to work together to perform tasks as a team. Robots designed for such collaboration must reason about how their task-completion strategies interplay with the behavior and skills of their human team members as they coordinate on achieving joint goals. Our goal in this work is to develop a computational framework for robot adaptation to human partners in human-robot team collaborations.
Observer-Aware Legibility for Social Navigation
2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)2022 We designed an observer-aware method for creating navigation paths that simultaneously indicate a robot’s goal while attempting to remain in view for a particular observer. Prior art in legible motion does not account for the limited field of view of observers, which can lead to wasted communication efforts that are unobserved by the intended audience. Our observer-aware legibility algorithm directly models the locations and perspectives of observers, and places legible movements where they can be easily seen.
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