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Found 16 papers, 4 papers with code
Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders
Multi-agent coordination is crucial for reliable multi-robot navigation in shared spaces such as automated warehouses.
Diverse Controllable Diffusion Policy with Signal Temporal Logic
We first calibrate the STL on the real-world data, then generate diverse synthetic data using trajectory optimization, and finally learn the rectified diffusion policy on the augmented dataset.
Signal Temporal Logic Neural Predictive Control
We conduct experiments on six tasks, where our method with the backup policy outperforms the classical methods (MPC, STL-solver), model-free and model-based RL methods in STL satisfaction rate, especially on tasks with complex STL specifications while being 10X-100X faster than the classical methods.
Hybrid Systems Neural Control with Region-of-Attraction Planner
For each system mode, we first learn an NN Lyapunov function and an NN controller to ensure the states within the region of attraction (RoA) can be stabilized.
ConBaT: Control Barrier Transformer for Safe Policy Learning
In this work, we propose Control Barrier Transformer (ConBaT), an approach that learns safe behaviors from demonstrations in a self-supervised fashion.
Case Studies for Computing Density of Reachable States for Safe Autonomous Motion Planning
Recent work provides a data-driven approach to compute the density distribution of autonomous systems' forward reachable states online.
Decentralized Coordinated State Estimation in Integrated Transmission and Distribution Systems
Current transmission and distribution system states are mostly unobservable to each other, and state estimation is separately conducted in the two systems owing to the differences in network structures and analytical models.
Learning Density Distribution of Reachable States for Autonomous Systems
State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazardous situations.
Reactive and Safe Road User Simulations using Neural Barrier Certificates
Reactive and safe agent modelings are important for nowadays traffic simulator designs and safe planning applications.
Localization and Mapping using Instance-specific Mesh Models
We show that the errors between projections of the mesh model and the observed keypoints and masks can be differentiated in order to obtain accurate instance-specific object shapes.
VA-RED$^2$: Video Adaptive Redundancy Reduction
An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
AdaFuse: Adaptive Temporal Fusion Network for Efficient Action Recognition
Temporal modelling is the key for efficient video action recognition.
Effects of impulsive harvesting and an evolving domain in a diffusive logistic model
In order to understand how the combination of domain evolution and impulsive harvesting affect the dynamics of a population, we propose a diffusive logistic population model with impulsive harvesting on a periodically evolving domain.
Analysis of PDEs
AR-Net: Adaptive Frame Resolution for Efficient Action Recognition
Specifically, given a video frame, a policy network is used to decide what input resolution should be used for processing by the action recognition model, with the goal of improving both accuracy and efficiency.
Learning 3D-aware Egocentric Spatial-Temporal Interaction via Graph Convolutional Networks
First, we decompose egocentric interactions into ego-thing and ego-stuff interaction, modeled by two GCNs.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error).
Ranked #71 on
Monocular Depth Estimation
on KITTI Eigen split
