Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-BasedRobotic Control

Robots That See Ahead: Simple Camera Learning for Moving Stuff

A robot that learns from its camera, and then can move things in ways you ask, sounds like future but it is happening now.
A vision-based system watches what happens next and uses a predictive model to choose actions, so it can push, pick, or rearrange objects.
You can tell the robot what to do by clicking a spot in a photo, showing a photo of the goal, or giving an example picture of success — each way is easy for anyone to use.
The robot was left to practice on hundreds of toys and objects, gathering autonomous data without someone needing to guide it, and that helps it to work on items it never saw before.
It even handles soft things like cloth, and hard things like cups.
This approach helps a robot generalize — meaning it can solve new tasks using the same learned skill — and it does this from plain camera images, no fancy sensors.
Small mistakes still happen, but the results are promising and useful for everyday tasks around the home or shop.

Read article comprehensive review in Paperium.net:
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-BasedRobotic Control

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