Abstract
New research on Robotics is the subject of a report. According to news reporting out of Shanghai, People's Republic of China, by NewsRx editors, research stated, “Robotic autonomous assembly is critical in intelligent manufacturing and has always been a research hotspot. Most previous approaches rely on prior knowledge, such as geometric parameters and pose information of the assembled parts, which are hard to estimate in unstructured environments.” Financial support for this research came from China's National Key Research and Development Program. Our news journalists obtained a quote from the research from Shanghai Jiao Tong University, “This paper proposes a residual reinforcement learning (RL) policy for robotic assembly via combining visual and force information. The residual RL policy, which consists of a visual-based policy and a force-based policy, is trained and tested in an end-to-end manner. In the assembly procedure, the visual-based policy focuses on spatial search, while the force-based policy handles the interactive behaviors. The experimental results reveal the high sample efficiency of our approach, which exhibits the ability to generalize across diverse assembly tasks involving variations in geometries, clearances, and configurations.”
NETL
NSTL
Elsevier