Method for controlling a robotic device

A robot control unit, which implements a neural convolutional network, a neural actor network, and a neural critic network and is configured to

• obtain an image of surroundings of the robotic device;
• process the image using the neural convolutional network, which generates an image in a feature space from the image, the image in the feature space including a vector in the feature space for each pixel of at least a subset of pixels of the image;
• feed the image in the feature space to the neural actor network, which generates an action parameter image from the image in the feature space, the action parameter image for each of the pixels including a set of action parameter values for an action of the robotic device;
• feed the image in the feature space and the action parameter image to the neural critic network, which generates an assessment image, which defines for each pixel an assessment for the action defined by the set of action parameter values for that pixel;
• select, from multiple sets of action parameters of the action parameter image, that set of action parameter values having the highest assessment;
• and
• control the robot for carrying out an action according to the selected action parameter set.

A non-transitory computer-readable medium on which is stored a computer program for controlling a robotic device, the computer program, when executed by a processor, causing the processor to perform the following steps:

• obtaining an image of surroundings of the robotic device;
• processing the image using a neural convolutional network, which generates an image in a feature space from the image, the image in the feature space including a vector in the feature space for each pixel of at least a subset of pixels of the image;
• feeding the image in the feature space to a neural actor network, which generates an action parameter image from the image in the feature space, the action parameter image for each of the pixels including a set of action parameter values for an action of the robotic device;
• feeding the image in the feature space and the action parameter image to a neural critic network, which generates an assessment image, which defines for each pixel an assessment for the action defined by the set of action parameter values for that pixel;
• selecting, from multiple sets of action parameters of the action parameter image, that set of action parameter values having the highest assessment;
• and
• controlling the robot for carrying out an action according to the selected action parameter set.