This invention presents a new kind of robots that learn in real-time, on the fly, without a need for either annotation of sensed images or annotation of motor images. Therefore, during the process of learning, such annotation-free robots are always conscious throughout its lifetime. This invention grew from the prior art called Developmental Networks that has already supported by its Emergent Turing Machine under-pinning and the maximum-likelihood property. These key properties make it practical to close the loop—from 3D world to 2D sensory images and motor images and back to 3D world. This invention seems to be the first algorithmic-level, holistic, and neural network model for developing machine consciousness. Furthermore, this model is through conscious learning and freedom from annotations of sensory images and motor images. This invention appears to be also the first to model animal-like discovery through general-purpose imitation.

Methods and systems for controlling a robot. In one aspect, the method (1300) comprises obtaining (s1302) first input information associated with a first simulation environment to which a first level of realism is assigned and obtaining (s1304) second input information associated with a second simulation environment to which a second level of realism is assigned. The first level of realism is different from the second level of realism. The method further comprises associating (s1306) the first input information with a first realism value representing the first level of realism; and associating (s1308) the second input information with a second realism value representing the second level of realism. The method further comprises modifying (s1310), based on the associated first input information and the associated second input information, one or more parameters of a machine learning (ML) process used for controlling the robot.

A robot system includes a robot, state detection sensors to, a timekeeping unit, a learning control unit, a determination unit, an operation device, and an input unit, and an additional learning unit. The determination unit determines whether or not the work of the robot can be continued under the control of the learning control unit based on the state values detected by the state detection sensors to and outputs determination result. The additional learning unit performs additional learning of the determination result indicating that the work of the robot cannot be continued, the operator operation force, work state output by the operation device and the input unit, and timer signal output by the timekeeping unit.

A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.

A computer-implemented method for training a classifier, particularly a binary classifier, for classifying input signals to optimize performance according to a non-decomposable metric that measures an alignment between classifications corresponding to input signals of a set of training data and corresponding predicted classifications of the input signals obtained from the classifier. The method includes providing weighting factors that characterize how the non-decomposable metric depends on a plurality of terms from a confusion matrix of the classifications and the predicted classifications, and training the classifier depending on the provided weighting factors.

A method for altering an initially predetermined path of a robot arrangement having at least one robot includes selecting a portion of the initially predetermined path, altering the selected portion of the path, and predetermining an altered path based on the altered portion of the path. A deviation between the initially predetermined path and the altered path is determined, and a reaction is triggered if the deviation fulfills a predetermined condition for a reaction. In another aspect, a computer programming product, when executed by a computer, causes the computer to select a portion of the initially predetermined path, alter the selected portion of the path, predetermine an altered path based on the altered portion of the path, determine a deviation between the initially predetermined and the altered path, and trigger a reaction if the deviation fulfills a predetermined condition for a reaction.