Artificial neural networks
Recent research on robotics and machine learning has focused on improving the accuracy and efficiency of plant reconstruction in complex agricultural environments. A study by researchers at Wageningen University and Research Center in the Netherlands has identified significant limitations in current methods and proposes a novel approach using self-supervised learning
Robotics industry
Research conducted by the University of Clermont-Auvergne has shed light on the current state of data management in smart farming, a sector that has experienced significant advancements in robotics and IoT technologies. However, these developments necessitate robust data management and processing guidelines to fully harness the potential of data and
Artificial neural networks
Recent research on robotics and machine learning has focused on improving the accuracy and efficiency of plant reconstruction in complex agricultural environments. A study by researchers at Wageningen University and Research Center in the Netherlands has identified significant limitations in current methods and proposes a novel approach using self-supervised learning
Robotics industry
Researchers at Hunan University have developed a novel authentication protocol, named Hidden Cross-Domain Authentication (HCDA), to address the security challenges in smart manufacturing systems. The HCDA protocol utilizes a blockchain consortium to securely record public keys and authentication parameters, while embodied intelligent robots perform symmetric-key encryption/decryption operations and one-way
Robotics industry
Researchers at Hunan University have developed a novel authentication protocol, named Hidden Cross-Domain Authentication (HCDA), to address the security challenges in smart manufacturing systems. The HCDA protocol utilizes a blockchain consortium to securely record public keys and authentication parameters, while embodied intelligent robots perform symmetric-key encryption/decryption operations and one-way
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
Robotics industry
Researchers at Zhejiang University have made a groundbreaking discovery in the field of robotics and automation, presenting a novel approach for simultaneous recognition of gestures and forces in prosthetic control. The study, published in Ieee Robotics and Automation Letters, introduces a neural-driven simultaneous recognition approach based on the motor unit
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
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
Robotics industry
Researchers from Zhejiang University have developed a new digital programming strategy that enables multi-material integration in liquid crystal elastomers (LCEs) via controlled network topology design. This breakthrough has the potential to significantly expand the design possibilities for LCE-based actuators, paving the way for advanced soft robotic material systems. By leveraging
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
Robotics industry
Researchers at Northeastern University have made significant breakthroughs in push-grasp policy learning, a crucial aspect of robotics and automation. The study, funded by the JPMorgan Chase PhD Fellowship, National Science Foundation, and National Aeronautics & Space Administration, proposes a novel framework for joint pushing and grasping policy learning. This framework,
