6 citations · 30 across the 14 of their papers we have counts for
14 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…
Monte Carlo Tree Search with Velocity Obstacles for safe and efficient motion planning in dynamic environments
Lorenzo Bonanni, Daniele Meli, Alberto Castellini +1
Online motion planning is a challenging problem for intelligent robots moving in dense environments with dynamic obstacles, e.g., crowds. In this work, we propose a novel approach…
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…
I2EDL: Interactive Instruction Error Detection and Localization
Francesco Taioli, Stefano Rosa, Alberto Castellini +5
In the Vision-and-Language Navigation in Continuous Environments (VLN-CE) task, the human user guides an autonomous agent to reach a target goal via a series of low-level actions f…
Mind the Error! Detection and Localization of Instruction Errors in Vision-and-Language Navigation
Francesco Taioli, Stefano Rosa, Alberto Castellini +5
Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target go…
Learning Logic Specifications for Policy Guidance in POMDPs: an Inductive Logic Programming Approach
Daniele Meli, Alberto Castellini, Alessandro Farinelli
Partially Observable Markov Decision Processes (POMDPs) are a powerful framework for planning under uncertainty. They allow to model state uncertainty as a belief probability distr…
Learning Logic Specifications for Soft Policy Guidance in POMCP
Giulio Mazzi, Daniele Meli, Alberto Castellini +1
Partially Observable Monte Carlo Planning (POMCP) is an efficient solver for Partially Observable Markov Decision Processes (POMDPs). It allows scaling to large state spaces by com…
Unsupervised Active Visual Search with Monte Carlo planning under Uncertain Detections
Francesco Taioli, Francesco Giuliari, Yiming Wang +6
We propose a solution for Active Visual Search of objects in an environment, whose 2D floor map is the only known information. Our solution has three key features that make it more…
POMP++: Pomcp-based Active Visual Search in unknown indoor environments
Francesco Giuliari, Alberto Castellini, Riccardo Berra +5
In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning s…
Rule-based Shielding for Partially Observable Monte-Carlo Planning
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
Identification of Unexpected Decisions in Partially Observable Monte-Carlo Planning: a Rule-Based Approach
Giulio Mazzi, Alberto Castellini, Alessandro Farinelli
Partially Observable Monte-Carlo Planning (POMCP) is a powerful online algorithm able to generate approximate policies for large Partially Observable Markov Decision Processes. The…
POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments
Yiming Wang, Francesco Giuliari, Riccardo Berra +5
In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method use…