3 citations · 5 across the 9 of their papers we have counts for
5 papers · 1 filter
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,…