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Found 7 papers, 1 papers with code
Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing
Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios, ranging from pattern recognition to complex robotic problems.
Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
Identifying safe areas is a key point to guarantee trust for systems that are based on Deep Neural Networks (DNNs).
Safe Deep Reinforcement Learning by Verifying Task-Level Properties
In this paper, we investigate an alternative approach that uses domain knowledge to quantify the risk in the proximity of such states by defining a violation metric.
Online Safety Property Collection and Refinement for Safe Deep Reinforcement Learning in Mapless Navigation
Our evaluation compares the benefits of computing the number of violations using standard hard-coded properties and the ones generated with CROP.
The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networks
Due to the #P-completeness of the problem, we also propose a randomized, approximate method that provides a provable probabilistic bound of the correct count while significantly reducing computational requirements.
Verifying Learning-Based Robotic Navigation Systems
Our work is the first to establish the usefulness of DNN verification in identifying and filtering out suboptimal DRL policies in real-world robots, and we believe that the methods presented here are applicable to a wide range of systems that incorporate deep-learning-based agents.
Curriculum Learning for Safe Mapless Navigation
To this end, we present a CL approach that leverages Transfer of Learning (ToL) and fine-tuning in a Unity-based simulation with the Robotnik Kairos as a robotic agent.
