Mitchell Hebert — accepted AI-conference papers

2025

Iteratively Adding Latent Human Knowledge Within Trajectory Optimization Specifications Improves Learning and Task Outcomes

Christine T. Chang, Maria P. Stull, Breanne Crockett, Emily Jensen, Clare Lohrmann, Mitchell Hebert +1

RA-L 2025

Frictionless and understandable tasking is essential for leveraging human-autonomy teaming in commercial, military, and public safety applications. Existing technology for facilitating human teaming with uncrewed aerial vehicles (UAVs), utilizing planners or trajectory optimizers that incorporate hu

Cited by 1 Source ↗Scholar ↗

2023

Human Non-Compliance with Robot Spatial Ownership Communicated via Augmented Reality: Implications for Human-Robot Teaming Safety

Christine T. Chang, Matthew B. Luebbers, Mitchell Hebert, Bradley Hayes

ICRA 2023poster

Ensuring the safety and efficiency of human workers in environments shared with autonomous robots is of paramount importance. In this work we examine the behavior and attitudes of participants performing tasks in a noisy environment collocated with an autonomous quadcopter robot. Visual communicatio…

Cited by 5 Source ↗Scholar ↗

2018

Real-Time Object Pose Estimation with Pose Interpreter Networks

Jimmy Wu, Bolei Zhou, Rebecca Russell, Vincent Kee, Syler Wagner, Mitchell Hebert +2

IROS 2018poster

In this work, we introduce pose interpreter networks for 6-DoF object pose estimation. In contrast to other CNN-based approaches to pose estimation that require expensively annotated object pose data, our pose interpreter network is trained entirely on synthetic pose data. We use object masks as an…

Cited by 59 Source ↗code ↗Scholar ↗

2017

SegICP: Integrated deep semantic segmentation and pose estimation

Jay M. Wong, Vincent Kee, Tiffany Le, Syler Wagner, Gian-Luca Mariottini, Abraham Schneider +7

IROS 2017poster

Recent robotic manipulation competitions have highlighted that sophisticated robots still struggle to achieve fast and reliable perception of task-relevant objects in complex, realistic scenarios. To improve these systems' perceptive speed and robustness, we present SegICP, a novel integrated soluti…

Cited by 189 Source ↗Scholar ↗

2016

Affordance-based Active Belief: Recognition using visual and manual actions

Dirk Ruiken, Jay Ming Wong, Tiffany Q. Liu, Mitchell Hebert, Takeshi Takahashi, Michael W. Lanighan +1

IROS 2016poster

This paper presents an active, model-based recognition system. It applies information theoretic measures in a belief-driven planning framework to recognize objects using the history of visual and manual interactions and to select the most informative actions. A generalization of the aspect graph is…

Cited by 12 Source ↗Scholar ↗