12 citations · 31 across the 9 of their papers we have counts for
9 papers
Signal Temporal Logic Neural Predictive Control
Yue Meng, Chuchu Fan
Ensuring safety and meeting temporal specifications are critical challenges for long-term robotic tasks. Signal temporal logic (STL) has been widely used to systematically and rigo…
A competition model with impulsive interventions and environmental perturbations in moving environments
Yue Meng, Zhigui Lin, Carlos Alberto Santos
In order to understand how impulsive interventions and environmental perturbations affect dynamics of competitors, we focus on a diffusive competition model with free boundaries an…
Crossover from viscous fingering to fracturing in cohesive wet granular media: a photoporomechanics study
Yue Meng, Wei Li, Ruben Juanes
We study fluid-induced deformation and fracture of cohesive granular media, and apply photoporomechanics to uncover the underpinning grain-scale mechanics. We fabricate photoelasti…
ConBaT: Control Barrier Transformer for Safe Policy Learning
Yue Meng, Sai Vemprala, Rogerio Bonatti +2
Large-scale self-supervised models have recently revolutionized our ability to perform a variety of tasks within the vision and language domains. However, using such models for aut…
Density Planner: Minimizing Collision Risk in Motion Planning with Dynamic Obstacles using Density-based Reachability
Laura Lützow, Yue Meng, Andres Chavez Armijos +1
Uncertainty is prevalent in robotics. Due to measurement noise and complex dynamics, we cannot estimate the exact system and environment state. Since conservative motion planners a…
Reactive and Safe Road User Simulations using Neural Barrier Certificates
Yue Meng, Zengyi Qin, Chuchu Fan
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications. In this work, we proposed a reactive agent model which can en…
Localization and Mapping using Instance-specific Mesh Models
Qiaojun Feng, Yue Meng, Mo Shan +1
This paper focuses on building semantic maps, containing object poses and shapes, using a monocular camera. This is an important problem because robots need rich understanding of g…
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks
Chengxi Li, Yue Meng, Stanley H. Chan +1
To enable intelligent automated driving systems, a promising strategy is to understand how human drives and interacts with road users in complicated driving situations. In this pap…
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Yue Meng, Yongxi Lu, Aman Raj +5
Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable…