Robotics Domain
Robotics research currently focuses on enhancing robot autonomy, safety, and dexterity, particularly in unstructured environments. Key areas include developing robust control algorithms (like Model Predictive Control and reinforcement learning), improving perception through advanced sensor fusion and generative models, and creating more efficient and adaptable robot designs. These advancements are driving progress in diverse applications such as agriculture, healthcare, and manufacturing, ultimately aiming to create more capable and reliable robots for a wider range of tasks.
Papers
Grasp Learning: Models, Methods, and Performance
Robert Platt
ART/ATK: A research platform for assessing and mitigating the sim-to-real gap in robotics and autonomous vehicle engineering
Asher Elmquist, Aaron Young, Thomas Hansen, Sriram Ashokkumar, Stefan Caldararu, Abhiraj Dashora, Ishaan Mahajan, Harry Zhang, Luning Fang, He Shen, Xiangru Xu, Radu Serban, Dan Negrut
Interactive Imitation Learning in Robotics: A Survey
Carlos Celemin, Rodrigo Pérez-Dattari, Eugenio Chisari, Giovanni Franzese, Leandro de Souza Rosa, Ravi Prakash, Zlatan Ajanović, Marta Ferraz, Abhinav Valada, Jens Kober
Reinforcement Learning for Solving Robotic Reaching Tasks in the Neurorobotics Platform
Márton Szép, Leander Lauenburg, Kevin Farkas, Xiyan Su, Chuanlong Zang
Rob\'otica M\'ovel e Intelig\^encia Artificial para Investiga\c{c}\~ao, Competi\c{c}\~ao e Automatiza\c{c}\~ao de Sistemas Industriais
Hiago Jacobs Sodre Pereira, Pablo Ezequiel Moraes, André Da Silva Kelbouscas, Ricardo Grando
From Modelling to Understanding Children's Behaviour in the Context of Robotics and Social Artificial Intelligence
Serge Thill, Vicky Charisi, Tony Belpaeme, Ana Paiva