Graph neural networks that model face-face interactions between meshes

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations for simulating a state of an environment over a sequence of time steps, the operations comprising, at each of one or more time steps:
• obtaining an environment mesh representing the state of the environment at the time step, wherein environment mesh includes:
• (i) a first object mesh representing a first object in the environment, and (ii) a second object mesh representing a second object in the environment;
• generating a graph representing the state of the environment at the time step, wherein the graph comprises a plurality of graph nodes and a plurality of graph edges, and wherein generating the graph comprises:
• determining that a first face of the first object mesh is within a collision distance of a second face of the second object mesh;
• and
• in response, instantiating a face-face edge in the graph, wherein the face-face edge is a graph edge that connects:
• (i) a first set of graph nodes in the graph that represent the first face in the first object mesh, and (ii) a second set of graph nodes in the graph that represent the second face in the second object mesh;
• processing the graph using a graph neural network to generate an updated graph;
• and
• generating an updated environment mesh representing the state of the environment at a next time step using the updated graph.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations for simulating a state of an environment over a sequence of time steps, the operations comprising, at each of one or more time steps:

• obtaining an environment mesh representing the state of the environment at the time step, wherein environment mesh includes:
• (i) a first object mesh representing a first object in the environment, and (ii) a second object mesh representing a second object in the environment;
• generating a graph representing the state of the environment at the time step, wherein the graph comprises a plurality of graph nodes and a plurality of graph edges, and wherein generating the graph comprises:
• determining that a first face of the first object mesh is within a collision distance of a second face of the second object mesh;
• and
• in response, instantiating a face-face edge in the graph, wherein the face-face edge is a graph edge that connects:
• (i) a first set of graph nodes in the graph that represent the first face in the first object mesh, and (ii) a second set of graph nodes in the graph that represent the second face in the second object mesh;
• processing the graph using a graph neural network to generate an updated graph;
• and
• generating an updated environment mesh representing the state of the environment at a next time step using the updated graph.