Robotic Arm
Robotic arms are being actively researched to enhance their capabilities in diverse applications, from industrial automation to assistive technologies and space exploration. Current research emphasizes improving control algorithms (e.g., model predictive control, reinforcement learning) and developing more adaptable designs, including hybrid mechanisms and soft robotics, to handle complex tasks and interact safely with humans and unstructured environments. These advancements are driving progress in areas like human-robot collaboration, deformable object manipulation, and precise control for applications such as surgery and assistive robotics, ultimately impacting various fields through increased efficiency and improved human-machine interaction.
Papers
Learning deformable linear object dynamics from a single trajectory
Shamil Mamedov, A. René Geist, Ruan Viljoen, Sebastian Trimpe, Jan Swevers
Development of a semi-autonomous framework for NDT inspection with a tilting aerial platform
Salvatore Marcellini, Simone D'Angelo, Alessandro De Crescenzo, Michele Marolla, Vincenzo Lippiello, Bruno Siciliano
Improving Robotic Arms through Natural Language Processing, Computer Vision, and Edge Computing
Pascal Sikorski, Kaleb Yu, Lucy Billadeau, Flavio Esposito, Hadi AliAkbarpour, Madi Babaiasl
"Pass the butter": A study on desktop-classic multitasking robotic arm based on advanced YOLOv7 and BERT
Haohua Que, Wenbin Pan, Jie Xu, Hao Luo, Pei Wang, Li Zhang