Mobile intelligent robots are being deployed in increasingly unstructured environments, where they are expected to work out complex and dynamic tasks such as autonomous movement and mobile manipulation. Such learning-based robots not only need to acquire basic information about their environments, but must also build this understanding with respect to factors such as object detection and semantic classification. Typically, a static model pretrained on a variety of data is deployed in a particular learning-based robot system. A robot expected to understand semantics, i.e. what is happening in a scene, would therefore learn how to do so during its pretraining phase. This approach poses three main challenges: the model may need to be retrained to incorporate new data; acquired knowledge should be preserved while adapting to new tasks and environments; and training signals of the environment are required during deployment.

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Artificial Intelligence, in its current wave, is primarily centered on Machine Learning, including deep learning.

AI and Machine Learning are used so interchangeably these days. Deep Learning is a new form of Machine Learning that is showing up in AI solutions these days. So let's understand the basic difference between each of these terms.