Search Results for author:
Found 6 papers, 2 papers with code
A Correct-and-Certify Approach to Self-Supervise Object Pose Estimators via Ensemble Self-Training
Real-world robotics applications demand object pose estimation methods that work reliably across a variety of scenarios.
Leveraging Large (Visual) Language Models for Robot 3D Scene Understanding
Abstract semantic 3D scene understanding is a problem of critical importance in robotics.
Certifiable 3D Object Pose Estimation: Foundations, Learning Models, and Self-Training
Our third contribution is a novel self-supervised training approach that uses our certificate of observable correctness to provide the supervisory signal to C-3PO during training.
Extracting Zero-shot Common Sense from Large Language Models for Robot 3D Scene Understanding
Semantic 3D scene understanding is a problem of critical importance in robotics.
Neural Trees for Learning on Graphs
We also prove that the number of parameters needed to achieve an $\epsilon$-approximation of the distribution function is exponential in the treewidth of the input graph, but linear in its size.
A Theory of Uncertainty Variables for State Estimation and Inference
Probability theory starts with a distribution function (equivalently a probability measure) as a primitive and builds all other useful concepts, such as law of total probability, Bayes' law, independence, graphical models, point estimate, on it.
