A new robotic suction cup that can grasp rough, curved and heavy stone, has been developed by scientists at the University of Bristol. The team, based at Bristol Robotics Laboratory, studied the structures of octopus biological suckers, which have superb adaptive suction abilities enabling them to anchor to rock.A new robotic suction cup that can grasp rough, curved and heavy stone, has been developed by scientists at the University of Bristol. The team, based at Bristol Robotics Laboratory, studied the structures of octopus biological suckers, which have superb adaptive suction abilities enabling them to anchor to rock.[#item_full_content]
A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.Robotics[#item_full_content]
A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.A team of roboticists at the University of California, Berkeley, reports that it is possible to train robots to do relatively simple tasks by using sim-to-real reinforcement learning to train them. In their study, published in the journal Science Robotics, the group trained a robot to walk in unfamiliar environments while it carried different loads, all without toppling over.[#item_full_content]
A team of roboticists and mechanical and aeronautical engineers at Stanford University has developed a spider-like robot for possible use in exploring caves or lava tubes on Mars. In their paper published in the journal Science Robotics, the group describes their reasons for developing the new robot, their inspiration for the design, and how well it worked when tested in a real-world environment.A team of roboticists and mechanical and aeronautical engineers at Stanford University has developed a spider-like robot for possible use in exploring caves or lava tubes on Mars. In their paper published in the journal Science Robotics, the group describes their reasons for developing the new robot, their inspiration for the design, and how well it worked when tested in a real-world environment.[#item_full_content]
Soft skin coverings and touch sensors have emerged as a promising feature for robots that are both safer and more intuitive for human interaction, but they are expensive and difficult to make. A recent study demonstrates that soft skin pads doubling as sensors made from thermoplastic urethane can be efficiently manufactured using 3D printers.Soft skin coverings and touch sensors have emerged as a promising feature for robots that are both safer and more intuitive for human interaction, but they are expensive and difficult to make. A recent study demonstrates that soft skin pads doubling as sensors made from thermoplastic urethane can be efficiently manufactured using 3D printers.[#item_full_content]
Generative adversarial networks (GANs) are widely used to synthesize intricate and realistic data by learning the distribution of authentic real samples. However, a significant challenge that GANs face is mode collapse, where the diversity of generated samples is notably lower than that of real samples. The complexity of GANs and their training process has made it difficult to reveal the underlying mechanism of mode collapse.Generative adversarial networks (GANs) are widely used to synthesize intricate and realistic data by learning the distribution of authentic real samples. However, a significant challenge that GANs face is mode collapse, where the diversity of generated samples is notably lower than that of real samples. The complexity of GANs and their training process has made it difficult to reveal the underlying mechanism of mode collapse.Machine learning & AI[#item_full_content]
A new study from NC State University combines three-dimensional embroidery techniques with machine learning to create a fabric-based sensor that can control electronic devices through touch. The paper is published in the journal Device.A new study from NC State University combines three-dimensional embroidery techniques with machine learning to create a fabric-based sensor that can control electronic devices through touch. The paper is published in the journal Device.Electronics & Semiconductors[#item_full_content]
Maura R. Grossman, JD, Ph.D., is a Research Professor at the Cheriton School of Computer Science, cross-appointed to the School of Public Health Sciences at Waterloo, an Adjunct Professor at Osgoode Hall Law School, and an affiliate faculty member of the Vector Institute for Artificial Intelligence. She is also a Principal at Maura Grossman Law, an eDiscovery law and consulting firm in Buffalo, New York.Maura R. Grossman, JD, Ph.D., is a Research Professor at the Cheriton School of Computer Science, cross-appointed to the School of Public Health Sciences at Waterloo, an Adjunct Professor at Osgoode Hall Law School, and an affiliate faculty member of the Vector Institute for Artificial Intelligence. She is also a Principal at Maura Grossman Law, an eDiscovery law and consulting firm in Buffalo, New York.Business[#item_full_content]
Researchers at Tohoku University and the University of California, Santa Barbara, have unveiled a probabilistic computer prototype. Manufacturable with a near-future technology, the prototype combines a complementary metal-oxide semiconductor (CMOS) circuit with a limited number of stochastic nanomagnets, creating a heterogeneous probabilistic computer.Researchers at Tohoku University and the University of California, Santa Barbara, have unveiled a probabilistic computer prototype. Manufacturable with a near-future technology, the prototype combines a complementary metal-oxide semiconductor (CMOS) circuit with a limited number of stochastic nanomagnets, creating a heterogeneous probabilistic computer.[#item_full_content]
Robots with wheels could potentially navigate a variety of indoor and outdoor environments, traveling for longer distances and with fewer risks of losing balance. While some wheeled robots have achieved very promising results in recent years, most of them are unable to reliably overcome steps (i.e., surfaces that are raised above ground level).Robots with wheels could potentially navigate a variety of indoor and outdoor environments, traveling for longer distances and with fewer risks of losing balance. While some wheeled robots have achieved very promising results in recent years, most of them are unable to reliably overcome steps (i.e., surfaces that are raised above ground level).[#item_full_content]
