8 citations · 9 across the 4 of their papers we have counts for
6 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…
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…
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…