Assistive Manipulation Through Intent Recognition
An upper body mobility limitation can severely impact a person's quality of life. Such limitations can prevent people from performing everyday tasks such as picking up a cup or opening a door. The U.S. Census Bureau has indicated that more than 8.2% of the U.S. population, or 19.9 million Americans, suffer from upper body limitations. Assistive robots offer a way for people with severe mobility impairment to complete daily tasks. However, current assistive robots primarily operate through teleoperation, which requires significant cognitive and physical effort from the user. We explore how these assistive robots can be improved with artificial intelligence to take an active role in helping their users. Drawing from our understanding of human verbal and nonverbal behaviors (like speech and eye gaze) during robot teleoperation, we study how intelligent robots can predict human intent during a task and assist toward task completion. We aim to develop technology to decrease operator fatigue and task duration when using assistive robots by employing human-sensitive shared autonomy.
Eye Gaze for Assistive Manipulation.
Reuben M. Aronson, Henny Admoni. HRI Pioneers workshop. 2020. pdf
Semantic Gaze Labeling for Human-Robot Shared Manipulation.
Reuben M. Aronson and Henny Admoni. Proceedings of the ACM Symposium on Eye Tracking Research and Applications (ETRA). 2019. pdf
Gaze for Error Detection During Human-Robot Shared Manipulation.
Reuben M. Aronson and Henny Admoni. Towards a Framework for Joint Action Workshop at RSS. 2018. pdf
Eye-Hand Behavior in Human-Robot Shared Manipulation.
Reuben M. Aronson, Thiago Santini, Thomas C. Kübler, Enkelejda Kasneci, Siddhartha Srinivasa, and Henny Admoni. Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI). 2018. pdf
Uncertainty Estimation and Resolution in Task Transfer
Adaptability is an essential skill in human cognition, enabling us to draw from our extensive, life-long experiences with various objects and tasks in order to address novel problems. To date, robots do not have this kind of adaptability, and yet, as our expectations of robots’ interactive and assistive capacity grows, it will be increasingly important for them to adapt to unpredictable environments in a similar manner as humans.
We explore how different types of interaction enable a robot to address novel task variations. Prior work has shown how different types of transfer problems can be addressed via continued interaction between the teacher and robot. Using a variety of interaction types allows a robot to obtain different task information and then address transfer problems of various complexity, such as identifying object replacements and creative tool use. Our current work involves assessing the robot’s proficiency at a task; in order for a robot to attempt to address a novel task variation, it needs to assess what knowledge it needs and which interaction type is most likely to provide it.
Questions can be directed to Tesca.
Robot Self-Assesment (MURI)
When a robot is uncertain about how it should complete a task, it should ask a human teacher for help. Doing this, however, requires the robot to locate the source of its uncertainty and the most effective method of querying the teacher in order to resolve that uncertainty. We are developing methods to address both problems by modeling the robot’s expected and actual knowledge throughout completing a task or interacting with a teacher. Please contact Tesca Fitzgerald for more information.
Recognizing and Reacting to Human Needs Determined by Social Signals
Being able to identify which humans need help and when they need help will enable robots to spontaneously offer assistance when needed, as well as triage how their help can best be distributed. To perform this kind of assessment requires an understanding of how humans naturally communicate their needs to others, as well as a model of individuals and their needs over time. To achieve and demonstrate these goals, this project seeks to build a waiter robot that can anticipate customer needs and respond to them both when actively hailed or implicitly needed. This environment also showcases the challenge of finding these signals while humans are also engaged in human-human group interactions and are not solely focused on their robot collaborator. Successfully implementing this system can help improve restaurant efficiency, and provide insight into how to model human thinking.
This project has been completed, but questions can be directed to Ada.
Activity Recognition in Restaurants to Address Underlying Needs: A Case Study.
Ada V. Taylor, Roman Kaufman, Michael Huang, Henny Admoni. Proceedings of IEEE International Conference on Robot & Human Interactive Communication (RO-MAN). 2022. pdf