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Found 26 papers, 13 papers with code
Reinforcement Learning-based Receding Horizon Control using Adaptive Control Barrier Functions for Safety-Critical Systems
Unfortunately, both performance and solution feasibility can be significantly impacted by two key factors: (i) the selection of the cost function and associated parameters, and (ii) the calibration of parameters within the CBF-based constraints, which capture the trade-off between performance and conservativeness.
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning
Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset.
DeLF: Designing Learning Environments with Foundation Models
Reinforcement learning (RL) offers a capable and intuitive structure for the fundamental sequential decision-making problem.
Secure Control of Connected and Automated Vehicles Using Trust-Aware Robust Event-Triggered Control Barrier Functions
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to safely navigate through a conflict area (e. g., traffic intersections, merging roadways, roundabouts).
Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition
Two difficulties here make low-light image enhancement a challenging task; firstly, it needs to consider not only luminance restoration but also image contrast, image denoising and color distortion issues simultaneously.
OVLA: Neural Network Ownership Verification using Latent Watermarks
Existing ownership verification methods either modify or introduce constraints to the neural network parameters, which are accessible to an attacker in a white-box attack and can be harmful to the network's normal operation, or train the network to respond to specific watermarks in the inputs similar to data poisoning-based backdoor attacks, which are susceptible to backdoor removal techniques.
PAGAR: Taming Reward Misalignment in Inverse Reinforcement Learning-Based Imitation Learning with Protagonist Antagonist Guided Adversarial Reward
Many imitation learning (IL) algorithms employ inverse reinforcement learning (IRL) to infer the intrinsic reward function that an expert is implicitly optimizing for based on their demonstrated behaviors.
Merging control in mixed traffic with safety guarantees: a safe sequencing policy with optimal motion control
We address the problem of merging traffic from two roadways consisting of both Connected Autonomous Vehicles (CAVs) and Human Driven Vehicles (HDVs).
Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles
We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area.
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
We also present a novel approach to propagate TMs more efficiently and precisely across ReLU activation functions.
RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements
Our approach, which incorporates the true position priors in the training process, is trained on inertial measurements and ground truth displacement data, allowing recursion and to learn both motion characteristics and systemic error bias and drift.
A Tool for Neural Network Global Robustness Certification and Training
Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines
A misspecified reward can degrade sample efficiency and induce undesired behaviors in reinforcement learning (RL) problems.
Programmatic Reward Design by Example
In this paper, we propose the idea of programmatic reward design, i. e. using programs to specify the reward functions in RL environments.
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees
By leveraging the piecewise linear nature of ReLU networks, our approach can efficiently construct a patch network tailored to the linear region where the buggy input resides, which when combined with the original network, provably corrects the behavior on the buggy input.
POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems
We present POLAR, a polynomial arithmetic-based framework for efficient bounded-time reachability analysis of neural-network controlled systems (NNCSs).
MISA: Online Defense of Trojaned Models using Misattributions
Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specific and potentially malicious ways.
DRIBO: Robust Deep Reinforcement Learning via Multi-View Information Bottleneck
This approach enables us to train high-performance policies that are robust to visual distractions and can generalize well to unseen environments.
Runtime-Safety-Guided Policy Repair
The key idea behind our approach is the formulation of a trajectory optimization problem that allows the joint reasoning of policy update and safety constraints.
Adversarial Training and Provable Robustness: A Tale of Two Objectives
We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks.
ReachNN: Reachability Analysis of Neural-Network Controlled Systems
In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i. e., as long as they ensure that the neural networks are Lipschitz continuous.
Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation
An important facet of reinforcement learning (RL) has to do with how the agent goes about exploring the environment.
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Recent work has identified that classification models implemented as neural networks are vulnerable to data-poisoning and Trojan attacks at training time.
Safety-Aware Apprenticeship Learning
Apprenticeship learning (AL) is a kind of Learning from Demonstration techniques where the reward function of a Markov Decision Process (MDP) is unknown to the learning agent and the agent has to derive a good policy by observing an expert's demonstrations.
ARSENAL: Automatic Requirements Specification Extraction from Natural Language
Requirements are informal and semi-formal descriptions of the expected behavior of a complex system from the viewpoints of its stakeholders (customers, users, operators, designers, and engineers).
