Experts Are Improving Robots’ Self-Awareness; Here’s How

Experts Are Improving Robots’ Self-Awareness; Here’s How

Experts are working on improving robots’ self-awareness, and here’s how they’re doing it. Check out the latest details below.

Boosting a robot’s self-awareness

A team of roboticists from the Munich Institute of Robotics and Machine Intelligence (MIRMI) at the Technical University of Munich in Germany, have discovered a way to provide robots with a certain degree of proprioception, which refers to the ability to sense the position and movement of one’s own body.

The study, which was published in the journal Science Robotics, involved developing a new machine-learning technique to enable robots to learn about their own physical characteristics.

To help robots move around in the real world, they are usually equipped with technology such as cameras and pressure sensors, which collect data that is then processed to control the robot’s legs and feet to perform the desired actions.

YouTube video

This passage explains how animals, including humans, use proprioception to know where their body parts are and how they can move and interact with their surroundings.

Proprioception is the ability of the brain to be aware of the state of the body. To achieve similar abilities in robots, researchers have employed machine learning techniques to add sensors that provide feedback on individual body parts.

The sensors can detect the position and movement of the knee joint, for example. By processing the data from these sensors, the robot can be made more aware of its overall body state, resulting in better control and interaction with its environment.

The researchers discovered that a robot can learn to understand its body without any pre-programmed data. They achieved this by using a technique called “motor babbling,” where all the servo motors in the robot are fired randomly.

This allows the robot to start building a database of information that can be used to learn how its parts work.

The researchers tested this approach on various kinds of robots, including a six-legged spider bot, a humanoid, and an arm.

They found that their method enabled all of the tested robots to develop an understanding of their own bodies, their parts, and how they function together.

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