241 citations · 750 across the 37 of their papers we have counts for
6 papers · 1 filter
Fast Object Segmentation Learning with Kernel-based Methods for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object segmentation is a key component in the visual system of a robot that performs tasks like grasping and object manipulation, especially in presence of occlusions. Like many ot…
Fast Region Proposal Learning for Object Detection for Robotics
Federico Ceola, Elisa Maiettini, Giulia Pasquale +2
Object detection is a fundamental task for robots to operate in unstructured environments. Today, there are several deep learning algorithms that solve this task with remarkable pe…
Compact Belief State Representation for Task Planning
Evgenii Safronov, Michele Colledanchise, Lorenzo Natale
Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a t…
Task Planning with Belief Behavior Trees
Evgenii Safronov, Michele Colledanchise, Lorenzo Natale
In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observa…
Act, Perceive, and Plan in Belief Space for Robot Localization
Michele Colledanchise, Damiano Malafronte, Lorenzo Natale
In this paper, we outline an interleaved acting and planning technique to rapidly reduce the uncertainty of the estimated robot's pose by perceiving relevant information from the e…
GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk
Fabrizio Bottarel, Giulia Vezzani, Ugo Pattacini +1
The use of benchmarks is a widespread and scientifically meaningful practice to validate performance of different approaches to the same task. In the context of robot grasping the…