KUKA robot sparse coding technology

Create Date: 2024-8-30 12:03:23|Source: KUKA

Sparse coding can be very useful in this regard. This is an unsupervised machine learning algorithm that is implemented by creating a "sparse dictionary" to represent newly acquired data. The primary robot uses spectral charts (from primitive pressure-sensitive drawings) to create such dictionaries as input data for sparse coding algorithms. The algorithm still outputs the same dictionary, but the output dictionary consists of data representing high-grade characteristics. Then, when a new crawl attempt is made, there is a new data input, and the newly entered raw data is converted into new high-grade feature data through this dictionary, which is called "sparse vectors". These sparse vectors are grouped according to the different events that caused the oscillation (or the success of the crawl).

KUKA robot sparse coding technology

It seems easy to make a robot aware of the slide out of an object, as the process of sliding out causes a series of oscillations. But how do you get a robot to tell the difference between oscillations that occur when an object slides out and oscillations that occur when a robot drags an object on another appearance, such as a tabletop? We must not forget that the movement of the robot arm itself also undergoes small oscillations. These three different events will have similar signals, but require the bot to take different actions. Identifying these different events is what machine learning is all about.

On a machine learning level, the two groups in the CoRo Lab have one thing in common: neither group adds artificial rules to machine learning algorithms. In other words, it is up to the robot's system to decide what data is relevant to the slide-out of the object (perhaps guessing the data about the scraping result, in the gripping guessing experimental group), rather than relying on the researcher to guess what the relevant data is.

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