Device and method for training a control policy for manipulating an object

A method for controlling a robot device, comprising the following steps:

• training a control policy for manipulating an object by:
• for each of one or more objects in each of one or more scenes:
• receiving an input data element including image data representing a shape of the object to be manipulated and a position of the object in the scene,
• generating, for each input data element, one or more training data elements by generating augmentations of the image data and pseudo-labels for the augmented image data according to a semi-supervised learning scheme, and
• training the control policy using the generated training data elements,
• wherein the training of the control policy includes determining a loss including loss terms for the generated training data elements, wherein:
• (i) each loss term is soft-weighted in a loss function by applying a softmax function to a confidence of pseudo-labels of manipulation poses of a respective training data element of the generated training data elements and/or (ii) loss terms are filtered out of the loss function when the confidence of pseudo-labels of manipulation poses of respective training data elements of the generated training data elements is below a predetermined threshold;
• receiving, for a scene in which the robot device should be controlled, further image data representing the scene;
• and
• supplying the obtained further image data to the control policy and generating a control signal for the robot device according to an output that the control policy generates in response to the obtained further image data.

A data processing device, configured to train a control policy for manipulating an object, the data processing device configured to:

• for each of one or more objects in each of one or more scenes:
• receive an input data element including image data representing a shape of the object to be manipulated and a position of the object in the scene;
• generate, for each input data element, one or more training data elements by generating augmentations of the image data and pseudo-labels for the augmented image data according to a semi-supervised learning scheme;
• and
• train the control policy using the generated training data elements;
• wherein the training of the control policy includes determining a loss including loss terms for the generated training data elements, wherein:
• (i) each loss term is soft-weighted in a loss function by applying a softmax function to a confidence of pseudo-labels of manipulation poses of a respective training data element of the generated training data elements and/or (ii) loss terms are filtered out of the loss function when the confidence of pseudo-labels of manipulation poses of respective training data elements of the generated training data elements is below a predetermined threshold.

A non-transitory computer-readable medium on which is stored a computer program including instructions for training a control policy for manipulating an object, the instructions, when executed by a computer, causing the computer to perform the following steps:

• for each of one or more objects in each of one or more scenes:
• receiving an input data element including image data representing a shape of the object to be manipulated and a position of the object in the scene;
• generating, for each input data element, one or more training data elements by generating augmentations of the image data and pseudo-labels for the augmented image data according to a semi-supervised learning scheme;
• and
• training the control policy using the generated training data elements;
• wherein the training of the control policy includes determining a loss including loss terms for the generated training data elements, wherein:
• (i) each loss term is soft-weighted in a loss function by applying a softmax function to a confidence of pseudo-labels of manipulation poses of a respective training data element of the generated training data elements, and/or (ii) loss terms are filtered out of the loss function when the confidence of pseudo-labels of manipulation poses of respective training data elements of the generated training data elements is below a predetermined threshold.