Articles tagged with Robot Control
Researchers created an artificial sensory receptor that generates spike signals on its own, enabling the e-skin to analyze spatial information and react to external stimuli in real-time. The e-skin's functionality overcomes limitations of conventional electronic skins, which can only process tactile information sequentially.
A new HMI system, Robotic VR, allows users to teleoperate robots with precision and feel, enabling complicated tasks such as Covid-19 swab tests and patient care. The system provides immersive feedback via Bluetooth, Wi-Fi, and the internet.
A new approach uses reinforcement learning algorithm to help robotic knee mimic intact human knee in walking, achieving 100% success rate on even ground. The technology also adapts to uneven terrain and changes in walking pace, promising a more comfortable experience for prosthetic users.
Researchers developed a recursive composite adaptive control method to improve the accuracy and dynamic performance of robot manipulators in the Chinese Space Station. The new controller reduces computational loads and achieves faster parameter convergence, making it suitable for high-degree-of-freedom robots.
New experiments challenge conventional wisdom on neuronal refractory periods, discovering durations exceeding 20 milliseconds and sensitivity to input signal origin. These findings may hold the key to understanding degenerative diseases and advancing artificial intelligence-based applications.
A new floating robotic film can hoover oil spills at sea or remove contaminants from drinking water, using a pulsing motion inspired by water striders. The film is powered by light and fueled by water, making it sustainable and reusable.
Researchers at MIT develop RFusion, a robotic system that uses data from a camera and radio frequency antenna to locate and retrieve lost items. The system relies on RFID tags and machine learning algorithms to optimize the robot's trajectory and grasp the object.
SUTD researchers develop sensor that assigns dirt score to areas based on visual and tactile analysis, allowing for more efficient exploration of complex spaces. The sensor is integrated with a smart algorithm that directs the robot to focus on areas with high dirt probability.
Cong Wang aims to develop a two-fingered robot that can perform everyday tasks with precision manipulation, using artificial intelligence and crowdsourcing. The robot will be taught by human volunteers through the Amazon Mechanical Turk system, with the goal of creating a physically intelligent being.
A new algorithm provides accurate solutions that mimic natural human movement, reducing the risk of injury. The Pro-ISADE approach improves calculation speed while ensuring calculated joint angles are feasible for robotic use in daily activities like drinking water and brushing teeth.
Researchers from Bar-Ilan University demonstrate the application of physical concepts in physics to solve key challenges in artificial intelligence. By adopting power-law scaling, they show that learning each example once is equivalent to learning examples repeatedly, enabling rapid decision-making and ultrafast learning.