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Found 24 papers, 6 papers with code
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
Moreover, we formally define the task of Instruction Error Detection and Localization, and establish an evaluation protocol on top of our benchmark dataset.
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty.
Analyzing Adversarial Inputs in Deep Reinforcement Learning
In recent years, Deep Reinforcement Learning (DRL) has become a popular paradigm in machine learning due to its successful applications to real-world and complex systems.
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).
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language
We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts.
Learning Logic Specifications for Soft Policy Guidance in POMCP
In this paper, we use inductive logic programming to learn logic specifications from traces of POMCP executions, i. e., sets of belief-action pairs generated by the planner.
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.
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications.
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.
An attention model for the formation of collectives in real-world domains
We consider the problem of forming collectives of agents for real-world applications aligned with Sustainable Development Goals (e. g., shared mobility, cooperative learning).
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.
Centralizing State-Values in Dueling Networks for Multi-Robot Reinforcement Learning Mapless Navigation
We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm.
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation
Aquatic navigation is an extremely challenging task due to the non-stationary environment and the uncertainties of the robotic platform, hence it is crucial to consider the safety aspect of the problem, by analyzing the behavior of the trained network to avoid dangerous situations (e. g., collisions).
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Results show that the shielded POMCP outperforms the standard POMCP in a case study in which a wrong parameter of POMCP makes it select wrong actions from time to time.
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning
The combination of Evolutionary Strategies (ES) and Deep Reinforcement Learning (DRL) has been recently proposed to merge the benefits of both solutions.
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
We propose an iterative process of trace analysis consisting of three main steps, i) the definition of a question by means of a parametric logical formula describing (probabilistic) relationships between beliefs and actions, ii) the generation of an answer by computing the parameters of the logical formula that maximize the number of satisfied clauses (solving a MAX-SMT problem), iii) the analysis of the generated logical formula and the related decision boundaries for identifying unexpected decisions made by POMCP with respect to the original question.
Evaluating the Safety of Deep Reinforcement Learning Models using Semi-Formal Verification
In this paper, we present a semi-formal verification approach for decision-making tasks, based on interval analysis, that addresses the computational demanding of previous verification frameworks and design metrics to measure the safety of the models.
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Our POMP method uses as input the current pose of an agent (e. g. a robot) and a RGB-D frame.
Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem.
Algorithms for Graph-Constrained Coalition Formation in the Real World
In this paper, we focus on a special case of coalition formation known as Graph-Constrained Coalition Formation (GCCF) whereby a network connecting the agents constrains the formation of coalitions.
Worst-case bounds on the quality of max-product fixed-points
We study worst-case bounds on the quality of any fixed point assignment of the max-product algorithm for Markov Random Fields (MRF).
