7 papers
Proceedings of 1st Workshop on Advancing Artificial Intelligence through Theory of Mind
Mouad Abrini, Omri Abend, Dina Acklin +105
This volume includes a selection of papers presented at the Workshop on Advancing Artificial Intelligence through Theory of Mind held at AAAI 2025 in Philadelphia US on 3rd March 2…
Second-order Theory of Mind for Human Teachers and Robot Learners
Patrick Callaghan, Reid Simmons, Henny Admoni
Confusing or otherwise unhelpful learner feedback creates or perpetuates erroneous beliefs that the teacher and learner have of each other, thereby increasing the cognitive burden…
Bi-Directional Mental Model Reconciliation for Human-Robot Interaction with Large Language Models
Nina Moorman, Michelle Zhao, Matthew B. Luebbers +5
In human-robot interactions, human and robot agents maintain internal mental models of their environment, their shared task, and each other. The accuracy of these representations d…
Conformalized Interactive Imitation Learning: Handling Expert Shift and Intermittent Feedback
Michelle Zhao, Reid Simmons, Henny Admoni +2
In interactive imitation learning (IL), uncertainty quantification offers a way for the learner (i.e. robot) to contend with distribution shifts encountered during deployment by ac…
Conformalized Teleoperation: Confidently Mapping Human Inputs to High-Dimensional Robot Actions
Michelle Zhao, Reid Simmons, Henny Admoni +1
Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing…
Closed-loop Teaching via Demonstrations to Improve Policy Transparency
Michael S. Lee, Reid Simmons, Henny Admoni
Demonstrations are a powerful way of increasing the transparency of AI policies. Though informative demonstrations may be selected a priori through the machine teaching paradigm, s…
Understanding Robot Minds: Leveraging Machine Teaching for Transparent Human-Robot Collaboration Across Diverse Groups
Suresh Kumaar Jayaraman, Reid Simmons, Aaron Steinfeld +1
In this work, we aim to improve transparency and efficacy in human-robot collaboration by developing machine teaching algorithms suitable for groups with varied learning capabiliti…