256 citations · 298 across the 12 of their papers we have counts for
12 papers
Sampling-Based Motion Planning with Scene Graphs Under Perception Constraints
Qingxi Meng, Emiliano Flores, Thai Duong +2
It will be increasingly common for robots to operate in cluttered human-centered environments such as homes, workplaces, and hospitals, where the robot is often tasked to maintain…
Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications
Zhiqin Qian, Ryan Diaz, Sangwon Seo +1
When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed. As AI agents tackle increasi…
Look as You Leap: Planning Simultaneous Motion and Perception for High-DOF Robots
Qingxi Meng, Emiliano Flores, Carlos Quintero-Peña +5
Most common tasks for robots in dynamic spaces require that the environment is regularly and actively perceived. The perception task considered in this work can represent a broad r…
IDIL: Imitation Learning of Intent-Driven Expert Behavior
Sangwon Seo, Vaibhav Unhelkar
When faced with accomplishing a task, human experts exhibit intentional behavior. Their unique intents shape their plans and decisions, resulting in experts demonstrating diverse b…
GO-DICE: Goal-Conditioned Option-Aware Offline Imitation Learning via Stationary Distribution Correction Estimation
Abhinav Jain, Vaibhav Unhelkar
Offline imitation learning (IL) refers to learning expert behavior solely from demonstrations, without any additional interaction with the environment. Despite significant advances…
I-CEE: Tailoring Explanations of Image Classification Models to User Expertise
Yao Rong, Peizhu Qian, Vaibhav Unhelkar +1
Effectively explaining decisions of black-box machine learning models is critical to responsible deployment of AI systems that rely on them. Recognizing their importance, the field…
Automated Task-Time Interventions to Improve Teamwork using Imitation Learning
Sangwon Seo, Bing Han, Vaibhav Unhelkar
Effective human-human and human-autonomy teamwork is critical but often challenging to perfect. The challenge is particularly relevant in time-critical domains, such as healthcare…
Towards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
Yao Rong, Tobias Leemann, Thai-trang Nguyen +6
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of e…
Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations
Sangwon Seo, Vaibhav V. Unhelkar
We present Bayesian Team Imitation Learner (BTIL), an imitation learning algorithm to model the behavior of teams performing sequential tasks in Markovian domains. In contrast to e…
A Bayesian Approach to Identifying Representational Errors
Ramya Ramakrishnan, Vaibhav Unhelkar, Ece Kamar +1
Trained AI systems and expert decision makers can make errors that are often difficult to identify and understand. Determining the root cause for these errors can improve future de…
Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare
Sangwon Seo, Lauren R. Kennedy-Metz, Marco A. Zenati +3
Shared mental models are critical to team success; however, in practice, team members may have misaligned models due to a variety of factors. In safety-critical domains (e.g., avia…
Learning Dense Rewards for Contact-Rich Manipulation Tasks
Zheng Wu, Wenzhao Lian, Vaibhav Unhelkar +2
Rewards play a crucial role in reinforcement learning. To arrive at the desired policy, the design of a suitable reward function often requires significant domain expertise as well…