Agenti Autonomi Incorporati: Quando la Robotica incontra il Ragionamento con Deep Learning
Abstract: The increase in available computing power and the Deep Learning revolution have allowed the exploration of new topics and frontiers in Artificial Intelligence research. A new field called Embodied Artificial Intelligence, which places at the intersection of Computer Vision, Robotics, and Decision Making, has been gaining importance during the last few years, as it aims to foster the development of smart autonomous robots and their deployment in society. The recent availability of large collections of 3D models for photorealistic robotic simulation has allowed faster and safe training of learning-based agents for millions of frames and a careful evaluation of their behavior before deploying the models on real robotic platforms. These intelligent agents are intended to perform a certain task in a possibly unknown environment. To this end, during the training in simulation, the agents learn to perform continuous interactions with the surroundings, such as gathering information from the environment, encoding and extracting useful cues for the task, and performing actions towards the final goal; where every action of the agent influences the interactions. This dissertation follows the complete creation process of embodied agents for indoor environments, from their concept to their implementation and deployment. In the first part of this work, we study the importance of building efficient representations of the agent's knowledge aimed at its understanding of the world and learning capabilities on the task to pursue. We devise and examine two alternative approaches to implicitly encode and maximize the information collected without the need for annotated data, which are usually costly and difficult to produce. The first explored method rewards actions that produce a significant change in the agent's knowledge or internal representation of the environment and is called Impact. The second approach, instead, is called Curiosity, and as human curiosity does, it encourages the agent to explore states of the environment where it can see or learn new things. The investigation of implicit representations for embodied agents is followed by a study of agents' behavior on various robotic tasks, both in simulated and real settings. Following, we investigate the last step for a successful implementation of an autonomous agent: the deployment of the trained models on a real robot. We study how to transfer the knowledge acquired in simulation into the real world, considering and coping with the architectural discrepancies between those worlds to minimize the degradation caused by the simulation-to-reality transfer. The final part of this work presents the acquisition and public release of a photo-realistic 3D model of an art gallery accompanied by a dataset for navigation. This contribution enlarges the number of datasets available in the literature and enables simulated robot navigation inside museums. With this thesis, we aim to contribute to research in Embodied AI and autonomous agents, in order to foster future work in this field. We present a detailed analysis of the procedure behind implementing an intelligent embodied agent, comprehending a thorough description of the current state-of-the-art in literature, technical explanations of the proposed methods, and accurate experimental studies on relevant robotic tasks.
Citation:
Bigazzi, Roberto "Agenti Autonomi Incorporati: Quando la Robotica incontra il Ragionamento con Deep Learning" 2023
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