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Found 7 papers, 1 papers with code
Targeted Adversarial Attacks against Neural Network Trajectory Predictors
To bridge this gap, in this paper, we propose a targeted adversarial attack against DNN models for trajectory forecasting tasks.
Accelerated Reinforcement Learning for Temporal Logic Control Objectives
To address this problem, we propose a novel accelerated model-based reinforcement learning (RL) algorithm for LTL control objectives that is capable of learning control policies significantly faster than related approaches.
Model-based Reinforcement Learning
reinforcement-learning
+1
Planning and Control of Multi-Robot-Object Systems under Temporal Logic Tasks and Uncertain Dynamics
We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties.
Model-Based Robust Adaptive Semantic Segmentation
To mitigate this challenge, in this paper, we propose model-based robust adaptive training algorithm (MRTAdapt), a new training algorithm to enhance the robustness of DNN-based semantic segmentation methods against natural variations that leverages model-based robust training algorithms and generative adversarial networks.
A Temporal Logic-Based Hierarchical Network Connectivity Controller
We show that these constraints along with the requirement of propagating information in the network can be captured by a Linear Temporal Logic (LTL) framework.
Real-Time Detectors for Digital and Physical Adversarial Inputs to Perception Systems
To highlight this, we demonstrate the efficiency of the proposed detector on ImageNet, a task that is computationally challenging for the majority of relevant defenses, and on physically attacked traffic signs that may be encountered in real-time autonomy applications.
Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees
Reinforcement Learning (RL) has emerged as an efficient method of choice for solving complex sequential decision making problems in automatic control, computer science, economics, and biology.
