![]() ![]() The learning process consist of repeated interactions. Since, given unlimited ressources, a brute force method testing every order combination at a reasonable resolution would achieve that, we are interested We are interested in the robot’s ability to learn how to reach any reachable state of the sensors, given access to its previous observations. Even with a small number of primtives, and given the continuous nature of the parameters, the space of possible orders is extremely large. This temporal combination is simple, yet expressive and effective. With the start date of each primitive, the robot can temporally stage the parallel execution of a set of orders. The robot is able to combine several motor primitives by sending multiple orders at once. Every motor primitive is designed to end after a finite amount of time. For example, given the primitive Spin(d, v) controlling a wheel with d the duration of the primitive, and v the target velocity of the wheel, a fully qualified order is (Spin, (3.5, 1.0), 2.0), which instruct the motor to make start making the wheel spin at 1.0 rad/s at time 2 s, and for 3.5 s. The start date delays the execution of the motor primitive. An motor primitive order is described by the motor primitive, a value for each parameter, and a start date. A motor primitive has a specific number of parameters with fixed boundary values. For example, in a wheeled robot, a motor primitive could control how and how long a specific wheel spins, with a target velocity and a duration parameter. Motor primitives have been shown to exist in biological organisms. The robot has several different action at its disposal, henceforth called motor primitives, which are hardcoded sequences of motor orders whose behavior is controlled through continuous parameters. We consider a robot equiped with multiple sensors and several motors. To our knowledge, no existing work addresses both those challenges. Additionally, we considers robotic agents that have several different actions at their disposal that can combine them temporally. We propose a broad expansion of the previous architecture, where the sensory space has 10+ dimensions, and relevant goal space are created and their interest evaluated by the algorithms through novel techniques. Moreover, the goal space was predefined by hand. Yet sensory spaces have remained limited to 2 or 3 dimensions, and the robot had only one type of action to consider. This method has yielded excellent results in experiment with motor spaces of high dimension. In this article, we will build on a particular intrinsically motivated, goal-oriented technique initiated by Baranes and Oudeyer, which defines the interest of an area of the sensorimotor space as the progress of the competence in reaching self-assigned goals in this area. Several intrinsically motivated learning techniques have been proposed. ![]() Another approach have stemmed from the field of developmental robotics, where inspiration from psychology and neuroscience research on animal and infant learning have highlighted the importance of curiosity in skill acquisition. To adress this issue, statistical learning techniques have focused on optimizing exploration policies to maximize various criteria in particular through active learning. This is why efficient explorations techniques are needed, where each interaction maximize the knowledge or competence gained through each interaction. Learning in those spaces also raises other challenges, because robot’s sensorimotors spaces are highly heterogeneous and multi-modal, with unreachable areas because of physical constraints, unlearnable areas because the actions of the agent do not have any influence on the sensors values, and yet other area where learning is made difficult by huge noise-to-signal ratios or requires the previous aquisition of other skills (e.g. Such spaces are too large to be explored exhaustively, an issue even more crucial in robotics given the expensive and slow nature of the physical interactions needed to gather training data. Yet, because of their complex bodies and multiple sensors, robots face highly-dimensional, unbounded, continuous sensorimotor spaces whose semantics are often unknown. Many of those situations cannot be anticipated at design time : autonomous learning capacities are needed to adapt to novel, unexpected conditions. Today’s robotic systems are given increasingly complex tasks in an increasing variety of situations such as object or social interaction. ![]()
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