54 citations · 211 across the 34 of their papers we have counts for
37 papers
Explaining Why Things Go Where They Go: Interpretable Constructs of Human Organizational Preferences
Emmanuel Fashae, Michael Burke, Leimin Tian +2
Robotic systems for household object rearrangement often rely on latent preference models inferred from human demonstrations. While effective at prediction, these models offer limi…
Sentiment Matters: An Analysis of 200 Human-SAV Interactions
Lirui Guo, Michael G. Burke, Wynita M. Griggs
Shared Autonomous Vehicles (SAVs) are likely to become an important part of the transportation system, making effective human-SAV interactions an important area of research. This p…
Heteroscedasticity of Denoising Score Matching with Generalised Smooth Noise
Juyan Zhang, Rhys Newbury, Xinyang Zhang +3
Score Matching (SM) is a powerful framework for estimating the log-density derivatives of a distribution without calculating its normalizing constants. This capability has made it…
Learning a Neural Association Network for Self-supervised Multi-Object Tracking
Shuai Li, Michael Burke, Subramanian Ramamoorthy +1
This paper introduces a novel framework to learn data association for multi-object tracking in a self-supervised manner. Fully-supervised learning methods are known to achieve exce…
A Probabilistic Model for Skill Acquisition with Switching Latent Feedback Controllers
Juyan Zhang, Dana Kulic, Michael Burke
Manipulation tasks often consist of subtasks, each representing a distinct skill. Mastering these skills is essential for robots, as it enhances their autonomy, efficiency, adaptab…
Influence-Based Reward Modulation for Implicit Communication in Human-Robot Interaction
Haoyang Jiang, Elizabeth A. Croft, Michael G. Burke
Communication is essential for successful interaction. In human-robot interaction, implicit communication holds the potential to enhance robots' understanding of human needs, emoti…