AIConfPaper
← Search

Thiago Boaventura

9 accepted papers

2026

Load-Bearing Assessment for Safe Locomotion of Quadruped Robots on Collapsing Terrain

Vivian Suzano Medeiros, Giovanni Battista Dessy, Thiago Boaventura, Marcelo Becker, Claudio Semini, Victor Barasuol
ICRA 2026poster

Collapsing terrains, often present in search and rescue missions or planetary exploration, pose significant challenges for quadruped robots. This paper introduces a robust locomotion framework for safe navigation over unstable surfaces by integrating terrain probing, load-bearing analysis, motion pl…

Cited by 0Source ↗PDF ↗Scholar ↗
2025

Load-Bearing Assessment for Safe Locomotion of Quadruped Robots on Collapsing Terrain

Vivian Suzano Medeiros, Giovanni Dessy, Thiago Boaventura, Marcelo Becker, Claudio Semini, Victor Barasuol
RA-L 2025

Collapsing terrains, often present in search and rescue missions or planetary exploration, pose significant challenges for quadruped robots. This paper introduces a robust locomotion framework for safe navigation over unstable surfaces by integrating terrain probing, load-bearing analysis, motion pl

Cited by 0Source ↗Scholar ↗
2024

HuMAn - the Human Motion Anticipation Algorithm Based on Recurrent Neural Networks

Victor Noppeney, Felix M. Escalante Ortega, Lucas Maggi, Thiago Boaventura
RA-L 2024

Predicting human motion may lead to considerable advantages for human-robot interaction, particularly when precise synchronization between the robot's motion and the user's movement is imperative. The inherent stochastic nature of human behavior, combined with the restricted window of response, can

Cited by 2Source ↗Scholar ↗
2023

Markovian Transparency Control of an Exoskeleton Robot

Felix M. Escalante Ortega, Leonardo F. dos Santos, Jose Y. Moreno, Adriano A. G. Siqueira, Marco H. Terra, Thiago Boaventura
RA-L 2023

In wearable robotics, certain applications require the robot to be transparent, i.e., imperceptible to the user. This is a very difficult cooperative control task due to the inherent coupling between human and robot, unpredictable human movements, and user-dependent behavior. In this letter, we prop

Cited by 10Source ↗Scholar ↗
2022

A hybrid model-based evolutionary optimization with passive boundaries for physical human-robot interaction

Gustavo J. G. Lahr, Henrique B. Garcia, Arash Ajoudani, Thiago Boaventura, Glauco A. P. Caurin
ICRA 2022poster

The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the r…

Cited by 1Source ↗Scholar ↗
2022

Editorial Variable Impedance Control and Learning in Complex Interaction Scenarios: Challenges and Opportunities

Fares J. Abu-Dakka, Matteo Saveriano, Meghan E. Huber, Thiago Boaventura
RA-L 2022

The papers in this special section focus on variable impedance control and learning in complex interaction applications. Increasingly, robots are expected to enter various application scenarios and interact with unknown and dynamically changing environments. More specifically, we are expecting robot

Cited by 1Source ↗Scholar ↗
2019

Joint kinematic configuration influence on the passivity of an impedance-controlled robotic leg

Felipe Y. G. Higa, Gustavo J. G. Lahr, Glauco A. P. Caurin, Thiago Boaventura
ICRA 2019poster

Although the design of legged robots may be dependent on the application, all of them share the need to safely deal with physical interaction with the environment in every step they take. Impedance controllers have been applied with success to handle contact, and some authors applied the concept of…

Cited by 2Source ↗Scholar ↗
2018

Online prediction of threading task failure using Convolutional Neural Networks

Guilherme R. Moreira, Gustavo J. G. Lahr, Thiago Boaventura, Jose O. Savazzi, Glauco A. P. Caurin
IROS 2018poster

Fasteners assembly automation in different industries require flexible systems capable of dealing with faulty situations. Fault detection and isolation (FDI) techniques are used to detect failure and deal with them, avoiding losses on parts, tools or robots. However, FDI usually deals with the fault…

Cited by 18Source ↗Scholar ↗