There are several key approaches for using images to train real-life robots.
Researchers collect a large dataset of labelled – clearly defined – images relevant to the robot’s task. This might include images of objects that the robot needs to interact with and the environments it will operate in. The images should be annotated with relevant information, for example, object locations and classifications.
Afterwards, they use deep learning techniques like convolutional neural networks (CNNs) to train computer vision models. These models can learn to recognize objects or estimate poses. Researchers combine computer vision models with reinforcement learning to train so-called end-to-end visuomotor capabilities or policies. The robot can learn to map raw image inputs directly to control actions.
To achieve a real-life condition in the training of robots, researchers generate training images in simulation. This involves randomizing aspects like lighting, textures and camera angles to help the model generalize to real-world conditions. There are several further advanced techniques to optimize robotic visions. For example, researchers use few-shot learning and meta-learning to allow robots to quickly adapt their visual models to new objects or environments. Meta-learning, also known as ‘learning to learn,’ is a concept in machine learning where algorithms are designed to become better at learning over time. Self-improvement is key, where the robot can query for labels on the most informative images to optimize its models.
Please read two articles that are important for understanding image recognition. One is about deep learning. Another one is called The ‘Why’ Behind a Cuckoo Egg.
We recommend books, videos and articles as follows for more advanced readers:
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