Paulo Vinicius Koerich Borges — accepted AI-conference papers

2026

Deformable Cluster Manipulation Via Whole-Arm Policy Learning

Jayadeep Jacob, Wenzheng Zhang, Houston Warren, Paulo Vinicius Koerich Borges, Fabio Ramos, Tirthankar Bandyopadhyay

ICRA 2026poster

Manipulating clusters of deformable objects presents a substantial challenge with widespread applicability, but requires contact-rich whole-arm interactions. A potential solution must address the limited capacity for realistic model synthesis, high uncertainty in perception, and the lack of efficien…

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2024

Gentle Manipulation of Tree Branches: A Contact-Aware Policy Learning Approach

Jay Jacob, Shizhe Cai, Paulo Vinicius Koerich Borges, Tirthankar Bandyopadhyay, Fabio Ramos

CoRL 2024poster

Learning to interact with deformable tree branches with minimal damage is challenging due to their intricate geometry and inscrutable dynamics. Furthermore, traditional vision-based modelling systems suffer from implicit occlusions in dense foliage, severely changing lighting conditions, and limited…

Cited by 3 Source ↗Scholar ↗

2023

Air-Ground Collaborative Localisation in Forests Using Lidar Canopy Maps

Lucas Carvalho de Lima, Milad Ramezani, Paulo Vinicius Koerich Borges, Michael Brünig

RA-L 2023

Geo-localisation in GPS-poor environments such as forests is crucial in field robotics and remains a challenge. To tackle this problem, we introduce a collaborative localisation framework that fuses ‘above canopy’ height information obtained from airborne aggregated lidar scans, as a reference map,

Cited by 14 Source ↗Scholar ↗

2018

Colourising Point Clouds Using Independent Cameras

Pavel Vechersky, Mark Cox, Paulo Vinicius Koerich Borges, Thomas Lowe

RA-L 2018

We investigate the problem of colourising a point cloud using an arbitrary mobile mapping device and an independent camera. The loose coupling of sensors creates a number of challenges related to timing, point visibility, and colour determination. We address each of these problems and demonstrate ou

Cited by 27 Source ↗Scholar ↗

2015

Discrete-continuous clustering for obstacle detection using stereo vision

Robert Bichsel, Paulo Vinicius Koerich Borges

IROS 2015poster

Efficient obstacle detection is a key requirement for safe robot navigation. We consider the operation of autonomous vehicles in structured industrial environments. In such scenarios, an usual way to perform obstacle detection is to generate an estimate of the ground and detect elements that are on…

Cited by 6 Source ↗Scholar ↗

2015

Ground or obstacles? Detecting clear paths in vehicle navigation

Ralph Aeschimann, Paulo Vinicius Koerich Borges

ICRA 2015poster

Efficient estimation of obstacles is a crucial component in autonomous vehicle navigation. Fusing stereo vision and laser scan data is a widely used approach for obstacle detection and navigation. Stereo vision can detect obstacles and the ground. Planar laser range finders can detect obstacles, but…

Cited by 16 Source ↗Scholar ↗