Nicola A. Piga — accepted AI-conference papers

2024

Sim2Real Bilevel Adaptation for Object Surface Classification using Vision-Based Tactile Sensors

Gabriele M. Caddeo, Andrea Maracani, Paolo Didier Alfano, Nicola A. Piga, Lorenzo Rosasco, Lorenzo Natale

ICRA 2024poster

In this paper, we address the Sim2Real gap in the field of vision-based tactile sensors for classifying object surfaces. We train a Diffusion Model to bridge this gap using a relatively small dataset of real-world images randomly collected from unlabeled everyday objects via the DIGIT sensor. Subseq…

Cited by 2 Source ↗code ↗Scholar ↗

2023

Adaptive Tactile Force Control in a Parallel Gripper With Low Positioning Resolution

Nicola A. Piga, Lorenzo Natale

RA-L 2023

In this letter we develop an adaptive tactile force controller for a parallel gripper with low positioning resolution. We show both mathematically and experimentally that a standard integral controller is not suited to control the force in a closed-loop fashion as it induces oscillations in the syst

Cited by 6 Source ↗Scholar ↗

2023

Collision-aware In-hand 6D Object Pose Estimation using Multiple Vision-based Tactile Sensors

Gabriele M. Caddeo, Nicola A. Piga, Fabrizio Bottarel, Lorenzo Natale

ICRA 2023poster

In this paper, we address the problem of estimating the in-hand 6D pose of an object in contact with multiple vision-based tactile sensors. We reason on the possible spatial configurations of the sensors along the object surface. Specifically, we filter contact hypotheses using geometric reasoning a…

Cited by 17 Source ↗code ↗Scholar ↗

2023

Hybrid Object Tracking with Events and Frames

Zhichao Li, Nicola A. Piga, Franco Di Pietro, Massimiliano Iacono, Arren Glover, Lorenzo Natale +1

IROS 2023poster

Robust object pose tracking plays an important role in robot manipulation, but it is still an open issue for quickly moving targets as motion blur and low frequency detection can reduce pose estimation accuracy even for state-of-the-art RGB-D-based methods. An event-camera is a low-latency vision se…

Cited by 1 Source ↗code ↗Scholar ↗

2022

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

Nicola A. Piga, Yuriy Onyshchuk, Giulia Pasquale, Ugo Pattacini, Lorenzo Natale

RA-L 2022

6D object pose tracking has been extensively studied in the robotics and computer vision communities. The most promising solutions, leveraging on deep neural networks and/or filtering and optimization, exhibit notable performance on standard benchmarks. However, to our best knowledge, these have not

Cited by 26 Source ↗Scholar ↗