A computer-implemented control system for controlling a physical system, the control system comprising a computer-implemented neural network system for processing data representing the physical system, and a controller configured to use an output of the neural network system predicting one or more future states of the physical system to control one or more actions to be applied to the physical system, the physical system is an electromechanical system interacting with a real-world environment to perform a task, the computer-implemented neural network system for processing data representing the physical system comprising: an input to receive physical system data characterizing the physical system and action data, wherein the physical system comprises bodies coupled by joints, wherein the physical system data comprises at least dynamic data comprising, for each body, one or more of position data, orientation data, linear or angular velocity data, and acceleration data, the dynamic data representing a dynamic state defined by motion of the bodies of the physical system, and wherein the action data represents the one or more actions applied to the physical system; a graph processing neural network comprising one or more graph network blocks and trained to process an input graph (104; G) to provide an output graph (108; G*), wherein the graph processing neural network is configured to: for each of the edges, process the input graph edge features using an edge neural network ( f e) to determine the output graph edge features, for each of the nodes, aggregate the output graph edge features for edges connecting to the node in the graph structure to determine a set of aggregated edge features for the node, and for each of the nodes, process the aggregated edge features and the input graph node features using a node neural network ( f n) to determine the output graph node features; wherein the input and output graphs each have a graph structure comprising nodes and edges corresponding, respectively, to the bodies and joints of the physical system, wherein the input graph has input graph nodes comprising input graph node features representing the dynamic data and has input graph edges comprising input graph edge features representing the action data; and wherein the output graph has output graph nodes comprising output graph node features and output graph edges comprising output graph edge features, wherein the output graph node features comprise features for inferring a static property or dynamic state of the physical system; and an output to provide the inferred static property or dynamic state of the physical system, predicting the one or more future states of the physical system, to the controller to control the actions to be applied to the physical system. an input to receive physical system data characterizing the physical system and action data, wherein the physical system comprises bodies coupled by joints, wherein the physical system data comprises at least dynamic data comprising, for each body, one or more of position data, orientation data, linear or angular velocity data, and acceleration data, the dynamic data representing a dynamic state defined by motion of the bodies of the physical system, and wherein the action data represents the one or more actions applied to the physical system; a graph processing neural network comprising one or more graph network blocks and trained to process an input graph (104; G) to provide an output graph (108; G*), wherein the graph processing neural network is configured to: for each of the edges, process the input graph edge features using an edge neural network ( f e) to determine the output graph edge features, for each of the nodes, aggregate the output graph edge features for edges connecting to the node in the graph structure to determine a set of aggregated edge features for the node, and for each of the nodes, process the aggregated edge features and the input graph node features using a node neural network ( f n) to determine the output graph node features; wherein the input and output graphs each have a graph structure comprising nodes and edges corresponding, respectively, to the bodies and joints of the physical system, wherein the input graph has input graph nodes comprising input graph node features representing the dynamic data and has input graph edges comprising input graph edge features representing the action data; and wherein the output graph has output graph nodes comprising output graph node features and output graph edges comprising output graph edge features, wherein the output graph node features comprise features for inferring a static property or dynamic state of the physical system; and an output to provide the inferred static property or dynamic state of the physical system, predicting the one or more future states of the physical system, to the controller to control the actions to be applied to the physical system.