Humans exhibit remarkable proficiency in navigating and interacting with the 3D world using only 2D sensors like the eyes. Despite the apparent limitations of human retinas compared to advanced sensors like lidars or high-resolution cameras, humans can robustly interpret and respond to their surroundings. One of the keys lies in exploiting regularities in 3D space through purposeful movements, such as gaze fixation. Gaze fixation involves coordinated movements of the body and eyes, allowing humans, in principle, to extract relevant 3D properties by focusing on one object at a time during motion. Taking advantage of such coordinated movements has been demonstrated so far on a robot.
This thesis topic draws inspiration from gaze fixation to structure 3D perception on robots and complements it with Reinforcement Learning to learn optimal policies on top of that structured representation.
By combining gaze fixation with Reinforcement Learning, this thesis aims to quickly create new behaviors on the robot depending on a task description, ultimately paving the way for the development of efficient and adaptable robotic systems.
In this thesis, you will:
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