Abstract: According to the present invention, it is provided an interactive device comprising a display, a camera, an image analyzing means, said interactive device comprising means to acquire an image with the camera, the analyzing means detecting at least a human face on the acquired image and displaying on the display at least a pattern where the human face was detected wherein the interactive device further comprises means to determine a halo region extending at least around the pattern and means to add into the halo region at least one interactive zone related to a command, means to detect movement onto the interactive zone and means to execute the command by said device.

Abstract: Multiple parallel signal streams are produced by the sensors (20, 30, 40, 50) associated with a device (10). Events corresponding to real-world activities, contexts, environments, internal-states, uses, interactions and the like involving the device (10) can be detected in the multi-channel sensor-signal stream by evaluating how the pattern of coordination between the different sensors varies over time. The pattern of coordination between the sensors can be evaluated for a particular time period by computing an information distance matrix (IDM) for the sensor-signals generated during that time period. The IDM summarizes the normalized information distance between each possible pairing of the sensors.

Abstract: In order to promote efficient learning of relationships inherent in a system or setup S described by system-state and context parameters, the next action to take, affecting the setup, is determined based on the knowledge gain expected to result from this action. Knowledge-gain is assessed “locally” by comparing the value of a knowledge-indicator parameter after the action with the value of this indicator on one or more previous occasions when the system-state/context parameter(s) and action variable(s) had similar values to the current ones. Preferably the “level of knowledge” is assessed based on the accuracy of predictions made by a prediction module. This technique can be applied to train a prediction machine by causing it to participate in the selection of a sequence of actions. This technique can also be applied for managing development of a self-developing device or system, the self-developing device or system performing a sequence of actions selected according to the action-selection technique.

Abstract: Multiple parallel signal streams are produced by the sensors (20,30,40,50) associated with a device (10). Events corresponding to real-world activities, contexts, environments, internal-states, uses, interactions and the like involving the device (10) can be detected in the multi-channel sensor-signal stream by evaluating how the pattern of coordination between the different sensors varies over time. The pattern of coordination between the sensors can be evaluated for a particular time period by computing an information distance matrix (IDM) for the sensor-signals generated during that time period. The IDM summarizes the normalized information distance between each possible pairing of the sensors.

Abstract: According to the present invention, it is provided an interactive device comprising a display, a camera, an image analysing means, said interactive device comprising means to acquire an image with the camera, the analysing means detecting at least a human face on the acquired image and displaying on the display at least a pattern where the human face was detected wherein the interactive device further comprises means to determine a halo region extending at least around the pattern and means to add into the halo region at least one interactive zone related to a command, means to detect movement onto the interactive zone and means to execute the command by said device.

Abstract: Multiple parallel signal streams are produced by the sensors (20, 30, 40, 50) associated with a device (10). Events corresponding to real-world activities, contexts, environments, internal-states, uses, interactions and the like involving the device (10) can be detected in the multi-channel sensor-signal stream by evaluating how the pattern of coordination between the different sensors varies over time. The pattern of coordination between the sensors can be evaluated for a particular time period by computing an information distance matrix (IDM) for the sensor-signals generated during that time period. The IDM summarizes the normalized information distance between each possible pairing of the sensors.

Abstract: A self-developing device (1) capable of open-ended development makes use of a special motivational system for selecting which action should be taken on the environment by an associated sensory-motor apparatus (2). For a given candidate action, a motivational module (11) calculates a reward associated with the corresponding values that would be taken by one or more motivational variables that are independent of the nature of the associated sensory-motor apparatus. Preferred motivational variables are dependent on the developmental history of the device (1), and include variables quantifying the predictability, familiarity and stability of sensory-motor variables serving as the inputs to the device (1). The sensory-motor variables represent the status of the external environment and/or the internal resources (3) of the sensory-motor apparatus (2) whose behavior is controlled by the self-developing device (1).

Abstract: In order to promote efficient learning of relationships inherent in a system or setup S described by system-state and context parameters, the next action to take, affecting the setup, is determined based on the knowledge gain expected to result from this action. Knowledge-gain is assessed “locally” by comparing the value of a knowledge-indicator parameter after the action with the value of this indicator on one or more previous occasions when the system-state/context parameter(s) and action variable(s)=had similar values to the current ones. Preferably the “level of knowledge” is assessed based on the accuracy of predictions made by a prediction module. This technique can be applied to train a prediction machine by causing it to participate in the selection of a sequence of actions. This technique can also be applied for managing development of a self-developing device or system, the self-developing device or system performing a sequence of actions selected according to the action-selection technique.

Abstract: A self-developing device (1) capable of open-ended development makes use of a special motivational system for selecting which action should be taken on the environment by an associated sensory-motor apparatus (2). For a given candidate action, a motivational module (11) calculates a reward associated with the corresponding values that would be taken by one or more motivational variables that are independent of the nature of the associated sensory-motor apparatus. Preferred motivational variables are dependent on the developmental history of the device (1), and include variables quantifying the predictability, familiarity and stability of sensory-motor variables serving as the inputs to the device (1). The sensory-motor variables represent the status of the external environment and/or the internal resources (3) of the sensory-motor apparatus (2) whose behaviour is controlled by the self-developing device (1).

Abstract: A clicker-training technique developed for animal training is adapted for training robots, notably autonomous animal-like robots. In this robot-training method, a behaviour (for example, [DIG]) is broken down into smaller achievable responses ([SIT]—[HELLO]—[DIG]) that will eventually lead to the desired final behaviour. The robot is guided progressively to the correct behaviour through the use, normally the repeated use, of a secondary reinforcer. When the correct behaviour has been achieved, a primary reinforcer is applied so that the desired behaviour can be “captured”. This method can be used for training a robot to perform, on command, rare behaviours or a sequence of behaviours (typically actions). This method can also be used to ensure that a robot is focusing its attention upon a desired object.