45 citations · 108 across the 26 of their papers we have counts for
28 papers
BlenDAgger: Blended Shared Control for Interactive Imitation Learning
Cailyn Smith, Geoffrey Sun, Henny Admoni +1
Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators are not always optimal. We pro…
Teaching Robot Policies to Humans Using Erroneous Examples
Rithika Narayan, Suresh Kumaar Jayaraman, Henny Admoni
Human-robot collaboration describes the process of humans and autonomous agents working together to accomplish common goals. This process is facilitated best when robot policies, o…
Synchronizing Beliefs with Second-Order Theory-of-Mind in Human-Autonomy Teams (Extended Version)
Jack Mirenzi, Henny Admoni
Comparative feedback, asking people which of two behaviors they prefer, has become a standard way to align robot and agent behavior with human intent when the reward itself cannot…
What Do You Think I Think? Accounting for Human Beliefs Using Second-Order Theory of Mind
Patrick Callaghan, Reid Simmons, Henny Admoni
Discrepancies between an agent's actual knowledge and what a person thinks the agent knows can hinder interactions. If an agent could detect such discrepancies, it could provide fe…
Adapting by Analogy: OOD Generalization of Visuomotor Policies via Functional Correspondence
Pranay Gupta, Henny Admoni, Andrea Bajcsy
End-to-end visuomotor policies trained using behavior cloning have shown a remarkable ability to generate complex, multi-modal low-level robot behaviors. However, at deployment tim…
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…
The Sense of Agency in Assistive Robotics Using Shared Autonomy
Maggie A. Collier, Rithika Narayan, Henny Admoni
Sense of agency is one factor that influences people's preferences for robot assistance and a phenomenon from cognitive science that represents the experience of control over one's…
Towards an LLM-Based Speech Interface for Robot-Assisted Feeding
Jessie Yuan, Janavi Gupta, Akhil Padmanabha +4
Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who…
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…
DegustaBot: Zero-Shot Visual Preference Estimation for Personalized Multi-Object Rearrangement
Benjamin A. Newman, Pranay Gupta, Kris Kitani +3
De gustibus non est disputandum ("there is no accounting for others' tastes") is a common Latin maxim describing how many solutions in life are determined by people's personal pref…
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…
Bootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration
Benjamin A Newman, Chris Paxton, Kris Kitani +1
Agents that assist people need to have well-initialized policies that can adapt quickly to align with their partners' reward functions. Initializing policies to maximize performanc…
VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
Akhil Padmanabha, Jessie Yuan, Janavi Gupta +4
Physically assistive robots present an opportunity to significantly increase the well-being and independence of individuals with motor impairments or other forms of disability who…
Paper index: Designing an introductory HRI course (workshop at HRI 2024)
Henny Admoni, Daniel Szafir, Wafa Johal +1
Human-robot interaction is now an established discipline. Dozens of HRI courses exist at universities worldwide, and some institutions even offer degrees in HRI. However, although…
Object Importance Estimation using Counterfactual Reasoning for Intelligent Driving
Pranay Gupta, Abhijat Biswas, Henny Admoni +1
The ability to identify important objects in a complex and dynamic driving environment is essential for autonomous driving agents to make safe and efficient driving decisions. It a…
Multi-Agent Strategy Explanations for Human-Robot Collaboration
Ravi Pandya, Michelle Zhao, Changliu Liu +2
As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that…
Coordination with Humans via Strategy Matching
Michelle Zhao, Reid Simmons, Henny Admoni
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 strategie…
Towards Rich, Portable, and Large-Scale Pedestrian Data Collection
Allan Wang, Abhijat Biswas, Henny Admoni +1
Recently, pedestrian behavior research has shifted towards machine learning based methods and converged on the topic of modeling pedestrian interactions. For this, a large-scale da…
Reasoning about Counterfactuals to Improve Human Inverse Reinforcement Learning
Michael S. Lee, Henny Admoni, Reid Simmons
To collaborate well with robots, we must be able to understand their decision making. Humans naturally infer other agents' beliefs and desires by reasoning about their observable b…
DReyeVR: Democratizing Virtual Reality Driving Simulation for Behavioural & Interaction Research
Gustavo Silvera, Abhijat Biswas, Henny Admoni
Simulators are an essential tool for behavioural and interaction research on driving, due to the safety, cost, and experimental control issues of on-road driving experiments. The m…