Algorithms
New research from Beihua University has proposed an innovative method for optimizing the trajectory planning of industrial robotic arms, which aims to address the limitations of existing algorithms in efficiently and effectively navigating complex scenarios. The study integrates an improved sparrow search algorithm (NISSA) with elite reverse learning and Cauchy-Gaussian
Robotics
A patent for a robotic control system that allows for the autonomous manipulation of soft tissue and nonuniform objects has been published. The system, invented by Matthew DiCicco, Eric A. Litman, Charles Paul Pace, and David N. Walsh, enables a robotic manipulator to contact a deformable surface and apply a
Algorithms
New research from Beihua University has proposed an innovative method for optimizing the trajectory planning of industrial robotic arms, which aims to address the limitations of existing algorithms in efficiently and effectively navigating complex scenarios. The study integrates an improved sparrow search algorithm (NISSA) with elite reverse learning and Cauchy-Gaussian
Algorithms
New research from Beihua University has proposed an innovative method for optimizing the trajectory planning of industrial robotic arms, which aims to address the limitations of existing algorithms in efficiently and effectively navigating complex scenarios. The study integrates an improved sparrow search algorithm (NISSA) with elite reverse learning and Cauchy-Gaussian
Robotics
Researchers at Hangzhou Dianzi University have developed an adaptive event-triggered anti-windup trajectory tracking control method for a robotic manipulator system subject to uncertainties, external disturbances, component faults, and input saturation. This novel control method ensures the stability of the closed-loop system and has several advantages, including simplified design of the
Robotics
Researchers from Shenzhen University have proposed a new robot learning framework that integrates human multimodal demonstration knowledge to guide the reinforcement learning process, resulting in faster convergence and improved applicability to real-world robots. This framework is specifically designed for 3C assembly lines, where robots struggle with vast exploration spaces to
Artificial intelligence
Researchers from Shanghai Jiao Tong University have made significant breakthroughs in robotics assembly using reinforcement learning, overcoming the challenges of repetitive and sensitive tasks. The team's visuo-tactile approach has achieved remarkable success in terminal assembly, reducing collisions and ensuring parts are handled with precision. The approach involves decomposing
Artificial intelligence
Researchers from Shanghai Jiao Tong University have made significant breakthroughs in robotics assembly using reinforcement learning, overcoming the challenges of repetitive and sensitive tasks. The team's visuo-tactile approach has achieved remarkable success in terminal assembly, reducing collisions and ensuring parts are handled with precision. The approach involves decomposing
Robotics
Researchers from Shenzhen University have proposed a new robot learning framework that integrates human multimodal demonstration knowledge to guide the reinforcement learning process, resulting in faster convergence and improved applicability to real-world robots. This framework is specifically designed for 3C assembly lines, where robots struggle with vast exploration spaces to
Machine learning
Researchers from Technology and Innovation Centre have proposed a new algorithm called Constrained Expectation Maximization (CEM) to address a limitation in teaching motion skills to robots through demonstrations. The CEM algorithm enforces time-sensitive constraints (TSC) during the learning process, enabling improved and more efficient reproduction of demonstration data. This breakthrough
Robotics
Scientists at the Georgia Institute of Technology have discovered limitations in differential dynamic games (DDG) that often result in non-unique and infinite Nash Equilibria (NE). The study highlights the need for a novel framework to address the equilibrium coordination problem, which can lead to cost inefficiencies and catastrophic outcomes. Researchers
Robotics
Researchers at Mondragon University have successfully integrated human hints into reinforcement learning frameworks, significantly improving the adaptability and performance of disassembly tasks. The study reveals that manipulation generalization capabilities are enhanced when policies receive human hints regarding where to focus their actions. Employing force overlay types, such as Lissajous curves
