Research

Providing continuous physical and social support in human environments and social interactions requires new algorithmic approaches. Our research enables service robots to make effective use of computation to address the most critical elements of interaction with humans, while being flexible enough to support the full richness of human behavior. This includes developing fast, data-efficient algorithms for fluent interaction with groups and crowds, algorithms for learning on-the-fly and in-the-wild in noisy and uncontrolled environments, and understanding and addressing the needs of non-normative users, including children, older adults, and people with disabilities.


Research Areas

Fluent Interaction with Groups and Crowds

This work allows robots to quickly and naturally influence, understand, and learn from people in groups and crowds, while making minimal assumptions about the specifics of their behavior. Additionally, we develop fast new algorithms that allow robots to act as lively, responsive, and appealing social partners, while also accomplishing instrumental tasks relating to their embodiment (e.g., driving around a building to show a visitor where to go, or picking up objects to clear them off a table). Key research problems include:

  • Developing algorithms that are fast enough to function in "social time". Social interaction increases the importance of timing, since small changes can have social meaning (e.g., synchronization both reqsults from and reinforces rapport).
  • Modifying existing planning and learning algorithms to appropriately integrate instrumental actions relating to the robot's embodiment with necessary social behavior.
  • Designing new algorithms and architectures that allow robots to join and influence human-human interactions. Given the choice, users often interact with robots in groups, and the robot needs to be able to appropriately handle these interactions.

Learning on-the-Fly and in-the-Wild

General-purpose robots are unlikely to be deployed with all of the knowledge they need to complete every possible task. We expect that robots will need to learn after deployment, whether to customize their behavior to an individual user, or to learn to handle situations that were not anticipated by the robot designers. In particular, non-expert users in public spaces could be a rich source of information for robots, but the data obtained from these interaction is often noisy and sparse. Our work in this area both enables robots to learn more effectively from non-expert users in noisy real-world environments and equips robots with the social and interaction skills to help non-expert users provide more useful examples. Key research problems include:

  • Designing new types of interaction that result in better teaching from non-expert users, including improving their mental models of the robot's needs, providing more natural interfaces for teaching, and allowing the robot to actively request helpful examples.
  • Modifying existing learning algorithms with knowledge about common errors made my non-expert users in order to make them more robust to real-world teaching.
  • Developing new active learning algorithms that enable robots to participate in the learning process and acquire the most useful information from human teachers.

Understanding and Addressing the Needs of Non-Normative Users

Finally, our work is focused on understanding the needs of "non-normative" users, that is people like children, older adults, or people with disabilities, who are not included in the "convenience populations" with which much robotics research is done. Our goal is to use inclusive design and participartory research approaches to gain a better understanding of the needs of these users, and to develop new computational solutions that address real needs at the intersection of physical assistance and social support. Key research problems include:

  • Developing robots that can provide both physical assistance, support social integration, and maintain personal autonomy for people with disabilities.
  • Studying child-robot interaction, and solving the algorithmic challenges of supporting children's social, emotional, and cognitive development.
  • Designing new algorithms that allow robots to appropriately support older adults in their daily lives, especially in public spaces and places outside the home