7 papers
Learning to See While Learning to Act: Diffusion Models for Active Perception in Robot Imitation
Kuancheng Wang, Vaibhav Saxena, Shuo Cheng +2
Most imitation learning methods assume full observability in table-top settings. In practice, objects are often occluded, requiring robots to both search and act, and learning this…
Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation
Kuancheng Wang, Seungho Yeom, Jinglin Cao +3
Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action context, including prior at…
Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data
Paul Hoareau, Kuan Yi Wang, Brandon Bujak +6
INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing reliance on sparse 2D slices. We…
Evidential Physics-Informed Neural Networks for Scientific Discovery
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present the fundamental theory and implementation guidelines underlying Evidential Physics-Informed Neural Network (E-PINN) -- a novel class of uncertainty-aware PINN. It levera…
Evidential Physics-Informed Neural Networks
Hai Siong Tan, Kuancheng Wang, Rafe McBeth
We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quanti…
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Hai Siong Tan, Kuancheng Wang, Rafe Mcbeth
In this work, we examine the effectiveness of an uncertainty quantification framework known as Evidential Deep Learning applied in the context of biomedical image segmentation. Thi…