R. Capobianco

发表

Improved Learning of Dynamics Models for Control

Martial Hebert, Daniele Nardi, J. Andrew Bagnell, 2016, ISER.

Training Champion-level Race Car Drivers Using Deep Reinforcement Learning

Varun Raj Kompella, Peter R. Wurman, Michael D. Thomure, 2021 .

Using Variational Encoding and Generative Adversarial Learning for Inferring Missing Knowledge for Mobile Robots

D. Nardi, R. Capobianco, Francesco Riccio, 2020 .

Learning a Symbolic Planning Domain through the Interaction with Continuous Environments

Giuseppe De Giacomo, D. Nardi, R. Capobianco, 2021 .

Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal

R. Capobianco, R. Ragno, M. Mladenović, 2022, Journal of Computer-Aided Molecular Design.

Grounding LTLf Specifications in Images

Giuseppe De Giacomo, R. Capobianco, Elena Umili, 2022, NeSy.

A self-interpretable module for deep image classification on small data

D. Nardi, R. Capobianco, Biagio La Rosa, 2022, Applied Intelligence.

Agent-Based Markov Modeling for Improved COVID-19 Mitigation Policies

Peter R. Wurman, L. Meyers, R. Capobianco, 2021, J. Artif. Intell. Res..

Reinforcement Learning for Optimization of COVID-19 Mitigation policies pdf

Peter Stone, Roberto Capobianco, Spencer Fox, 2020, ArXiv.

Multiagent Epidemiologic Inference through Realtime Contact Tracing

Peter Stone, Guni Sharon, Roberto Capobianco, 2021, AAMAS.

Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning

Roberto Capobianco, Shreyansh Daftry, Giulia Ciabatti, 2021, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Outracing champion Gran Turismo drivers with deep reinforcement learning

Thomas J. Walsh, Peter R. Wurman, Michael D. Thomure, 2022, Nature.

LoOP: Iterative learning for optimistic planning on robots

Francesco Riccio, Roberto Capobianco, Daniele Nardi, 2021, Robotics Auton. Syst..

DOP: Deep Optimistic Planning with Approximate Value Function Evaluation pdf

Daniele Nardi, Francesco Riccio, Roberto Capobianco, 2018, AAMAS.

State of the Art of Visual Analytics for eXplainable Deep Learning

R. Capobianco, G. Santucci, D. Auber, 2023, Comput. Graph. Forum.

Knowledge-Based Reasoning on Semantic Maps

Luca Iocchi, Roberto Capobianco, Domenico Daniele Bloisi, 2014, AAAI 2014.

HI-VAL: Iterative Learning of Hierarchical Value Functions for Policy Generation

Daniele Nardi, Francesco Riccio, Roberto Capobianco, 2018, IAS.

Q-CP: Learning Action Values for Cooperative Planning

Daniele Nardi, Francesco Riccio, Roberto Capobianco, 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

S-AvE: Semantic Active Vision Exploration and Mapping of Indoor Environments for Mobile Robots

Francesco Riccio, Roberto Capobianco, Daniele Nardi, 2021, 2021 European Conference on Mobile Robots (ECMR).

Memory Wrap: a Data-Efficient and Interpretable Extension to Image Classification Models pdf

Daniele Nardi, Roberto Capobianco, Biagio La Rosa, 2021, ArXiv.

Grounding LTLf Specifications in Images

Giuseppe De Giacomo, R. Capobianco, Elena Umili, 2023, International Workshop on Neural-Symbolic Learning and Reasoning.

Efficient Long-term Mapping in Dynamic Environments

Maria Teresa Lazaro, Giorgio Grisetti, Roberto Capobianco, 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains

Jim Martin Catacora Ocana, D. Nardi, R. Capobianco, 2023, J. Artif. Intell. Res..

Exploration-Intensive Distractors: Two Environment Proposals and a Benchmarking

Jim Martin Catacora Ocana, D. Nardi, R. Capobianco, 2021, International Conference of the Italian Association for Artificial Intelligence.

Editorial: Ethical design of artificial intelligence-based systems for decision making

A. Milani, Giulio Biondi, R. Capobianco, 2023, Frontiers in artificial intelligence.

Living with Social Robots : Interactive Environmental Knowledge Acquisition

D. Nardi, R. Capobianco, L. Iocchi, 2015 .

Learning to Smooth with Bidirectional Predictive State Inference Machines

Byron Boots, Geoffrey J. Gordon, J. Andrew Bagnell, 2016, UAI.

Semi-Supervised GCN for learning Molecular Structure-Activity Relationships

R. Capobianco, Alessio Ragno, Dylan Savoia, 2022, ArXiv.

A self-interpretable module for deep image classification on small data

D. Nardi, R. Capobianco, B. La Rosa, 2021, Applied Intelligence.

A Discussion about Explainable Inference on Sequential Data via Memory-Tracking

Biagio La Rosa, R. Capobianco, Daniele Nardi, 2021, DP@AI*IA.

Planetary Environment Prediction Using Generative Modeling

R. Capobianco, S. Daftry, S. Singh, 2022, AIAA SCITECH 2022 Forum.