In recent years, roboticists have introduced robotic systems that can complete missions in various environments, ranging from the ground to underground, aboveground and underwater settings. While several of these robots can grasp and move objects on the ground, the handling of objects by robotic systems underwater has so far proved more challenging.
A team has shown that reinforcement learning -i.e., a neural network that learns the best action to perform at each moment based on a series of rewards- allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.
A research team has shown for the first time that reinforcement learning—i.e., a neural network that learns the best action to perform at each moment based on a series of rewards—allows autonomous vehicles and underwater robots to locate and carefully track marine objects and animals.
Roboticists have developed a jellyfish-inspired underwater robot with which they hope one day to collect waste from the bottom of the ocean. The almost noise-free prototype can trap objects underneath its body without physical contact, thereby enabling safe interactions in delicate environments such as coral reefs. Jellyfish-Bot could become an…