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João Carlos Virgolino Soares

4 accepted papers

2026

Proprioceptive Image: An Image Representation of Proprioceptive Data from Quadruped Robots for Contact Estimation Learning

Gabriel Fischer Abati, João Carlos Virgolino Soares, Giulio Turrisi, Victor Barasuol, Claudio Semini
ICRA 2026poster

This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for learning locomotion-related tasks. The proposed method encodes temporal dynamics from multiple propr…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

MUSE: A Real-Time Multi-Sensor State Estimator for Quadruped Robots

Ylenia Nisticò, João Carlos Virgolino Soares, Lorenzo Amatucci, Geoff Fink, Claudio Semini
RA-L 2025

This letter introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in (Fink et al. 2020). It integrate

Cited by 11Source ↗Scholar ↗
2025

Multi-Sensor Fusion for Quadruped Robot State Estimation Using Invariant Filtering and Smoothing

Ylenia Nisticò, Hajun Kim, João Carlos Virgolino Soares, Geoff Fink, Hae-Won Park, Claudio Semini
RA-L 2025

This letter introduces two multi-sensor state estimation frameworks for quadruped robots, built on the Invariant Extended Kalman Filter (InEKF) and Invariant Smoother (IS). The proposed methods, named E-InEKF and E-IS, fuse kinematics, IMU, LiDAR, and GPS data to mitigate position drift, particularl

Cited by 3Source ↗Scholar ↗
2025

SANDRO: A Robust Solver with a Splitting Strategy for Point Cloud Registration

Michael Adlerstein, João Carlos Virgolino Soares, Angelo Bratta, Claudio Semini
ICRA 2025

Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point cloud re

Cited by 2Source ↗code ↗Scholar ↗