Robot Arm
Robot arm research focuses on enhancing dexterity, control, and adaptability for diverse applications. Current efforts concentrate on improving control algorithms (e.g., reinforcement learning, bilateral control with transformers), developing advanced grippers (combining rigid and soft elements), and creating more intuitive human-robot interfaces (including haptic feedback and shared autonomy). These advancements are driving progress in areas like assistive robotics, manufacturing (e.g., additive manufacturing), and human-robot collaboration, ultimately aiming to create more versatile and useful robotic systems.
Papers
Embodied Self-Supervised Learning (EMSSL) with Sampling and Training Coordination for Robot Arm Inverse Kinematics Model Learning
Qu Weiming, Liu Tianlin, Wu Xihong, Luo Dingsheng
High-Precise Robot Arm Manipulation based on Online Iterative Learning and Forward Simulation with Positioning Error Below End-Effector Physical Minimum Displacement
Qu Weiming, Liu Tianlin, Luo Dingsheng
Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction
Shaunak A. Mehta, Dylan P. Losey
Wrapping Haptic Displays Around Robot Arms to Communicate Learning
Antonio Alvarez Valdivia, Soheil Habibian, Carly A. Mendenhall, Francesco Fuentes, Ritish Shailly, Dylan P. Losey, Laura H. Blumenschein