Search Results for author:
Found 23 papers, 5 papers with code
ChronoLLM: A Framework for Customizing Large Language Model for Digital Twins generalization based on PyChrono
Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research.
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty
We introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification.
CRISP: Object Pose and Shape Estimation with Test-Time Adaptation
Our first contribution is to introduce CRISP, a category-agnostic object pose and shape estimation pipeline.
Enhancing Autonomous Navigation by Imaging Hidden Objects using Single-Photon LiDAR
We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation.
SimBench: A Rule-Based Multi-Turn Interaction Benchmark for Evaluating an LLM's Ability to Generate Digital Twins
Given a collection of S-LLMs, this benchmark enables the ranking of the S-LLMs based on their ability to produce high-quality DTs.
Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting
The keyword spotting (KWS) problem requires large amounts of real speech training data to achieve high accuracy across diverse populations.
CHAMP: Conformalized 3D Human Multi-Hypothesis Pose Estimators
This process results in a differentiable conformal predictor that is trained end2end with the 3D pose estimator.
Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions
The new stability-augmented framework consists of a neural-network-based learner that learns to construct a Lyapunov function, and a model-based RL agent to consistently complete the tasks while satisfying user-specified constraints given only sub-optimal demonstrations and sparse-cost feedback.
Model-based Reinforcement Learning
Model Predictive Control
+2
Theoretical Analysis for Expectation-Maximization-Based Multi-Model 3D Registration
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-model 3D registration.
Learning Correspondence for Deformable Objects
We investigate the problem of pixelwise correspondence for deformable objects, namely cloth and rope, by comparing both classical and learning-based methods.
Multi-Model 3D Registration: Finding Multiple Moving Objects in Cluttered Point Clouds
We investigate a variation of the 3D registration problem, named multi-model 3D registration.
The Gift of Feedback: Improving ASR Model Quality by Learning from User Corrections through Federated Learning
Automatic speech recognition (ASR) models are typically trained on large datasets of transcribed speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
POLAR-Sim: Augmenting NASA's POLAR Dataset for Data-Driven Lunar Perception and Rover Simulation
For Task 2, one can produce as much data as desired to train and test AI algorithms that are anticipated to work in lunar conditions.
APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency
One underlying cause is that traditional diffusion models approximate Gaussian noise distribution by utilizing predictive noise, without fully accounting for the impact of inherent information within the input itself.
DiffCLIP: Leveraging Stable Diffusion for Language Grounded 3D Classification
Large pre-trained models have had a significant impact on computer vision by enabling multi-modal learning, where the CLIP model has achieved impressive results in image classification, object detection, and semantic segmentation.
TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation
We conjecture that this relationship is a generalizable notion of a manipulation task that can transfer to new objects in the same category; examples include the relationship between the pose of a pan relative to an oven or the pose of a mug relative to a mug rack.
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated Objects
We propose a vision-based system that learns to predict the potential motions of the parts of a variety of articulated objects to guide downstream motion planning of the system to articulate the objects.
Enabling On-Device Training of Speech Recognition Models with Federated Dropout
Federated learning can be used to train machine learning models on the edge on local data that never leave devices, providing privacy by default.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
We formulate a self-supervised objective for this problem and train a deep neural network to estimate the 3D rotation as parameterized by a quaternion, between these current and desired depth images.
Robots of the Lost Arc: Self-Supervised Learning to Dynamically Manipulate Fixed-Endpoint Cables
The framework finds a 3D apex point for the robot arm, which, together with a task-specific trajectory function, defines an arcing motion that dynamically manipulates the cable to perform tasks with varying obstacle and target locations.
6-DoF Grasp Planning using Fast 3D Reconstruction and Grasp Quality CNN
Recent consumer demand for home robots has accelerated performance of robotic grasping.
Personalization of End-to-end Speech Recognition On Mobile Devices For Named Entities
With speech input, if the user corrects only the names, the name recall rate improves to 64. 4%.
