Search Results for author: Ugo Pattacini

Found 8 papers, 5 papers with code

ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking

2 code implementations6 Nov 2021 Nicola A. Piga, Yuriy Onyshchuk, Giulia Pasquale, Ugo Pattacini, Lorenzo Natale

In this work, we introduce ROFT, a Kalman filtering approach for 6D object pose and velocity tracking from a stream of RGB-D images.

6D Pose Estimation using RGB Hand Pose Estimation +5

GRASPA 1.0: GRASPA is a Robot Arm graSping Performance benchmArk

1 code implementation12 Feb 2020 Fabrizio Bottarel, Giulia Vezzani, Ugo Pattacini, Lorenzo Natale

In this paper, we present version 1. 0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups.

Robotics

Sequence-to-Sequence Natural Language to Humanoid Robot Sign Language

no code implementations9 Jul 2019 Jennifer J. Gago, Valentina Vasco, Bartek Łukawski, Ugo Pattacini, Vadim Tikhanoff, Juan G. Victores, Carlos Balaguer

Natural language to sign language translation presents several challenges to developers, such as the discordance between the length of input and output data and the use of non-manual markers.

Sign Language Translation Translation

Markerless visual servoing on unknown objects for humanoid robot platforms

1 code implementation12 Oct 2017 Claudio Fantacci, Giulia Vezzani, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

To precisely reach for an object with a humanoid robot, it is of central importance to have good knowledge of both end-effector, object pose and shape.

Robotics Systems and Control Computation

Visual end-effector tracking using a 3D model-aided particle filter for humanoid robot platforms

1 code implementation14 Mar 2017 Claudio Fantacci, Ugo Pattacini, Vadim Tikhanoff, Lorenzo Natale

This paper addresses recursive markerless estimation of a robot's end-effector using visual observations from its cameras.

Robotics

Filter Forests for Learning Data-Dependent Convolutional Kernels

no code implementations CVPR 2014 Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.

Denoising

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