2 citations · 2 across the 9 of their papers we have counts for
10 papers
Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees
Harry Zhang, Nicolas Gorlo, Luca Carlone
Long-horizon robot operation requires spatio-temporal memory to record the environment state and recall it for downstream reasoning. Scene graphs and retrieval-augmented systems gr…
FUSE: Quantifying Uncertainty in Vision-Language Models by Bayesian Fusing Epistemic and Aleatoric Uncertainty
Harry Zhang, Luca Carlone
Vision-language models (VLMs) are playing an increasingly important role across multiple domains. In many applications, such as robotics, it is crucial to quantify the uncertainty…
H2OFlow: Grounding Human-Object Affordances with 3D Generative Models and Dense Diffused Flows
Harry Zhang, Luca Carlone
Understanding how humans interact with the surrounding environment, and specifically reasoning about object interactions and affordances, is a critical challenge in computer vision…
Max Entropy Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
Sangli Teng, Harry Zhang, David Jin +4
Designing optimal Bayes filters for nonlinear non-Gaussian systems is a challenging task. The main difficulties are: 1) representing complex beliefs, 2) handling non-Gaussian noise…
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty
Harry Zhang, Luca Carlone
We introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification. To improve on top of prior work, we d…
CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
Jingnan Shi, Rajat Talak, Harry Zhang +2
We consider the problem of estimating object pose and shape from an RGB-D image. Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation…
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators
Harry Zhang, Luca Carlone
We introduce CHAMP, a novel method for learning sequence-to-sequence, multi-hypothesis 3D human poses from 2D keypoints by leveraging a conditional distribution with a diffusion mo…
Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration
David Jin, Harry Zhang, Kai Chang
We perform detailed theoretical analysis of an expectation-maximization-based algorithm recently proposed in for solving a variation of the 3D registration problem, named multi-mod…
GMKF: Generalized Moment Kalman Filter for Polynomial Systems with Arbitrary Noise
Sangli Teng, Harry Zhang, David Jin +3
This paper develops a new filtering approach for state estimation in polynomial systems corrupted by arbitrary noise, which commonly arise in robotics. We first consider a batch se…
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds
David Jin, Sushrut Karmalkar, Harry Zhang +1
We investigate a variation of the 3D registration problem, named multi-model 3D registration. In the multi-model registration problem, we are given two point clouds picturing a set…