|
Rise of unmanned vehicles and autonomous robots has been accompanied by study of path planning, navigation, and decision-making algorithms. Current state-of-the-art employs deep neural nets to extract the required features. This technology, though successful in most cases, fails where the training has not been done for unseen conditions. For such unlabeled training data, transfer learning approaches have been proposed. A major drawback of using transfer learning approaches is that the actions and/or state spaces are reactive only to present circumstances. A truly intelligent autonomous operation has to consider a subordinate-to-a-human approach for its mission risks that vary with topography, path planning as well as mission goals. To address these complex combinatorial problems, DARPA has initiative a novel Explainable AI (XAI) technology in the past few years. In XAI, machine learning is paired with human intervention to make decisions by generating textual explanations of all the available relevant information / decision. In this paper, we propose to use available information along with human intelligence in a feedback loop for helping the unlabeled data to be trained and generate cost functions which were previously not programmed. We study this context/situation awareness to generate list of decision available from explanations on combinatorial tasks. Moreover, we employ this approach to a quadruped robot to learn its environment and the AI model starts in its infancy to mimic human cognitive architecture. We show that the learning process can be improved in a way that suits a particular mission in mind.
|
