31 citations · 178 across the 53 of their papers we have counts for
56 papers
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Yunhai Han, Jianuo Qiu, Linhao Bai +14
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception e…
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…
Towards Balancing Preference and Performance through Adaptive Personalized Explainability
Andrew Silva, Pradyumna Tambwekar, Mariah Schrum +1
As robots and digital assistants are deployed in the real world, these agents must be able to communicate their decision-making criteria to build trust, improve human-robot teaming…
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…
Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization
Brandon Ho, Batuhan Altundas, Matthew Gombolay
In fast-paced, ever-changing environments, dynamic Motion Planning for Multi-Agent Systems in the presence of obstacles is a universal and unsolved problem. Be it from path plannin…
Use of Winsome Robots for Understanding Human Feedback (UWU)
Jessica Eggers, Angela Dai, Matthew C. Gombolay
As social robots become more common, many have adopted cute aesthetics aiming to enhance user comfort and acceptance. However, the effect of this aesthetic choice on human feedback…
Compositional Instruction Following with Language Models and Reinforcement Learning
Vanya Cohen, Geraud Nangue Tasse, Nakul Gopalan +4
Combining reinforcement learning with language grounding is challenging as the agent needs to explore the environment while simultaneously learning multiple language-conditioned ta…
ELEMENTAL: Interactive Learning from Demonstrations and Vision-Language Models for Reward Design in Robotics
Letian Chen, Nina Moorman, Matthew Gombolay
Reinforcement learning (RL) has demonstrated compelling performance in robotic tasks, but its success often hinges on the design of complex, ad hoc reward functions. Researchers ha…
Designs for Enabling Collaboration in Human-Machine Teaming via Interactive and Explainable Systems
Rohan Paleja, Michael Munje, Kimberlee Chang +2
Collaborative robots and machine learning-based virtual agents are increasingly entering the human workspace with the aim of increasing productivity and enhancing safety. Despite t…
Mixed-Initiative Human-Robot Teaming under Suboptimality with Online Bayesian Adaptation
Manisha Natarajan, Chunyue Xue, Sanne van Waveren +2
For effective human-agent teaming, robots and other artificial intelligence (AI) agents must infer their human partner's abilities and behavioral response patterns and adapt accord…
Efficient Trajectory Forecasting and Generation with Conditional Flow Matching
Sean Ye, Matthew Gombolay
Trajectory prediction and generation are crucial for autonomous robots in dynamic environments. While prior research has typically focused on either prediction or generation, our a…
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial Games
Zixuan Wu, Sean Ye, Manisha Natarajan +1
Reinforcement Learning (RL)-based motion planning has recently shown the potential to outperform traditional approaches from autonomous navigation to robot manipulation. In this wo…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
Learning Models of Adversarial Agent Behavior under Partial Observability
Sean Ye, Manisha Natarajan, Zixuan Wu +3
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work,…
Diffusion Models for Multi-target Adversarial Tracking
Sean Ye, Manisha Natarajan, Zixuan Wu +1
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limite…
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan +3
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only h…
The Effect of Robot Skill Level and Communication in Rapid, Proximate Human-Robot Collaboration
Kin Man Lee, Arjun Krishna, Zulfiqar Zaidi +5
As high-speed, agile robots become more commonplace, these robots will have the potential to better aid and collaborate with humans. However, due to the increased agility and funct…
MAVERIC: A Data-Driven Approach to Personalized Autonomous Driving
Mariah L. Schrum, Emily Sumner, Matthew C. Gombolay +1
Personalization of autonomous vehicles (AV) may significantly increase trust, use, and acceptance. In particular, we hypothesize that the similarity of an AV's driving style compar…
Learning Coordination Policies over Heterogeneous Graphs for Human-Robot Teams via Recurrent Neural Schedule Propagation
Batuhan Altundas, Zheyuan Wang, Joshua Bishop +1
As human-robot collaboration increases in the workforce, it becomes essential for human-robot teams to coordinate efficiently and intuitively. Traditional approaches for human-robo…
Towards the design of user-centric strategy recommendation systems for collaborative Human-AI tasks
Lakshita Dodeja, Pradyumna Tambwekar, Erin Hedlund-Botti +1
Artificial Intelligence is being employed by humans to collaboratively solve complicated tasks for search and rescue, manufacturing, etc. Efficient teamwork can be achieved by unde…
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies
Pradyumna Tambwekar, Matthew Gombolay
Interactive Artificial Intelligence (AI) agents are becoming increasingly prevalent in society. However, application of such systems without understanding them can be problematic.…
Utilizing Human Feedback for Primitive Optimization in Wheelchair Tennis
Arjun Krishna, Zulfiqar Zaidi, Letian Chen +3
Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the t…
Safe Inverse Reinforcement Learning via Control Barrier Function
Yue Yang, Letian Chen, Matthew Gombolay
Learning from Demonstration (LfD) is a powerful method for enabling robots to perform novel tasks as it is often more tractable for a non-roboticist end-user to demonstrate the des…
FedPC: Federated Learning for Language Generation with Personal and Context Preference Embeddings
Andrew Silva, Pradyumna Tambwekar, Matthew Gombolay
Federated learning is a training paradigm that learns from multiple distributed users without aggregating data on a centralized server. Such a paradigm promises the ability to depl…