Categories: AI/ML News

Robots learn human-like movement adjustments to prevent object slipping

To effectively tackle a variety of real-world tasks, robots should be able to reliably grasp objects of different shapes, textures and sizes, without dropping them in undesired locations. Conventional approaches to enhancing the ability of robots to grasp objects work by tightening the grip of a robotic hand to prevent objects from slipping.

Aquatic robot’s self-learning optimization enhances underwater object manipulation skills

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…

November 7, 2024

In "AI/ML News"

Reinforcement learning allows underwater robots to locate and track objects underwater

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.

July 31, 2023

In "FAANG"