A system comprising: one or more computers arranged to implement a plurality of property detector neural networks (1201; 1301) and a further neural network, the one or more computers comprising a processor, and each property detector neural network arranged to receive data (1202; 1302) representing an object within a real-world environment containing a plurality of objects, and to generate respective property data associated with a property of the respective object; the processor being arranged to: receive an instruction (1204; 1304) indicating a task to be accomplished by a mechanical agent by interacting with the real-world environment, the task being associated with an object having an associated property; process the outputs of the plurality of property detector neural networks based upon the instruction to generate a relevance data item, the relevance data item indicating objects within the real-world environment associated with the task; generate a plurality of weights based upon the relevance data item; and generate modified data (1205; 1305) representing a plurality of the objects within the real-world environment based upon the plurality of weights; and the further neural network being arranged to receive the modified data and to output an action (1003; 1207; 1307) associated with the task, the action being a control input to control the mechanical agent. one or more computers arranged to implement a plurality of property detector neural networks (1201; 1301) and a further neural network, the one or more computers comprising a processor, and each property detector neural network arranged to receive data (1202; 1302) representing an object within a real-world environment containing a plurality of objects, and to generate respective property data associated with a property of the respective object; the processor being arranged to: receive an instruction (1204; 1304) indicating a task to be accomplished by a mechanical agent by interacting with the real-world environment, the task being associated with an object having an associated property; process the outputs of the plurality of property detector neural networks based upon the instruction to generate a relevance data item, the relevance data item indicating objects within the real-world environment associated with the task; generate a plurality of weights based upon the relevance data item; and generate modified data (1205; 1305) representing a plurality of the objects within the real-world environment based upon the plurality of weights; and receive an instruction (1204; 1304) indicating a task to be accomplished by a mechanical agent by interacting with the real-world environment, the task being associated with an object having an associated property; process the outputs of the plurality of property detector neural networks based upon the instruction to generate a relevance data item, the relevance data item indicating objects within the real-world environment associated with the task; generate a plurality of weights based upon the relevance data item; and generate modified data (1205; 1305) representing a plurality of the objects within the real-world environment based upon the plurality of weights; and the further neural network being arranged to receive the modified data and to output an action (1003; 1207; 1307) associated with the task, the action being a control input to control the mechanical agent.