Marrone Dantas

About

Marrone Dantas is a researcher at the forefront of intelligent robotics and human-robot collaboration, with a particular focus on safety, autonomy, and industrial maintenance. His work integrates deep learning, machine learning, and extreme learning models to enhance the perception and predictive capabilities of robotic systems. Dantas’s most cited paper, “Modeling and assessing an intelligent system for safety in human-robot collaboration using deep and machine learning techniques” (2021, 15 citations), introduces a framework that significantly improves safety protocols in shared workspaces. He further advances robotic autonomy in “A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models” (2022, 13 citations), enabling more precise and anticipatory robot behavior. His applied contributions include the design of a specialized gripper for radio base station maintenance (2021, 5 citations) and the development of the RBot system (2021, 3 citations), a robot-driven solution for autonomous infrastructure upkeep. Dantas’s work bridges theoretical machine learning with practical, safety-critical applications, making him a key contributor to the next generation of collaborative and autonomous industrial robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Modeling and assessing an intelligent system for safety in human-robot collaboration using deep and machine learning techniques

   15 citations · 2021
2. 2
   A framework for robotic arm pose estimation and movement prediction based on deep and extreme learning models

   13 citations · 2022
3. 3
   Gripper Design for Radio Base Station Autonomous Maintenance System

   5 citations · 2021
4. 4
   Rbot: development of a robot-driven radio base station maintenance system

   3 citations · 2021

Key Collaborators

Contact & Links