3 citations · 5 across the 9 of their papers we have counts for
9 papers
Benchmarking Interaction, Beyond Policy: a Reproducible Benchmark for Collaborative Instance Object Navigation
Edoardo Zorzi, Francesco Taioli, Yiming Wang +4
We propose Question-Asking Navigation (QAsk-Nav), the first reproducible benchmark for Collaborative Instance Object Navigation (CoIN) that enables an explicit, separate assessment…
Sentinel: Multi-Patch Transformer with Temporal and Channel Attention for Time Series Forecasting
Davide Villaboni, Alberto Castellini, Ivan Luciano Danesi +1
Transformer-based time series forecasting has recently gained strong interest due to the ability of transformers to model sequential data. Most of the state-of-the-art architecture…
Seldonian Reinforcement Learning for Ad Hoc Teamwork
Edoardo Zorzi, Alberto Castellini, Leonidas Bakopoulos +2
Most offline RL algorithms return optimal policies but do not provide statistical guarantees on desirable behaviors. This could generate reliability issues in safety-critical appli…
Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues
Francesco Taioli, Edoardo Zorzi, Gianni Franchi +4
Language-driven instance object navigation assumes that human users initiate the task by providing a detailed description of the target instance to the embodied agent. While this d…
Rigorous Probabilistic Guarantees for Robust Counterfactual Explanations
Luca Marzari, Francesco Leofante, Ferdinando Cicalese +1
We study the problem of assessing the robustness of counterfactual explanations for deep learning models. We focus on altering model parameters an…
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
Davide Corsi, Davide Camponogara, Alessandro Farinelli
An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes…
Planning and Inverse Kinematics of Hyper-Redundant Manipulators with VO-FABRIK
Cristian Morasso, Daniele Meli, Yann Divet +2
Hyper-redundant Robotic Manipulators (HRMs) offer great dexterity and flexibility of operation, but solving Inverse Kinematics (IK) is challenging. In this work, we introduce VO-FA…
Analyzing Adversarial Inputs in Deep Reinforcement Learning
Davide Corsi, Guy Amir, Guy Katz +1
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. However,…
Scaling #DNN-Verification Tools with Efficient Bound Propagation and Parallel Computing
Luca Marzari, Gabriele Roncolato, Alessandro Farinelli
Deep Neural Networks (DNNs) are powerful tools that have shown extraordinary results in many scenarios, ranging from pattern recognition to complex robotic problems. However, their…