Systems and Methods for Automatic Task Planning for Machine Tending Robotic Applications

A system comprising one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:

• receiving data defining one or more poses of a workpiece to be manipulated by a machine tended by a robot, wherein each pose specifies a position and an orientation of the workpiece with respect to the machine;
• generating, from each pose, a respective state node in a graph representing a state of the workpiece during the manipulation process;
• generating, for one or more state nodes, one or more grasp annotations that specify how the workpiece can be grasped by the robot in a state represented by the state node;
• generating, for each state node, one or more tasks including selecting, for each task, a respective grasp annotation that specifies how the robot will grasp the workpiece while performing the task;
• generating edges in the graph to connect state nodes in the grasp, each edge representing a respective task of tasks generated for the state nodes;
• selecting a path through the graph according to one or more task planning criteria;
• generating a behavior tree corresponding to the selected path, wherein leaves of the behavior tree are action nodes corresponding to the tasks represented by edges of the graph.

A non-transitory computer storage medium encoded with a computer program, the program comprising instructions that are operable, when executed by data processing apparatus to cause the data processing apparatus to perform operations comprising:

• receiving data defining one or more poses of a workpiece to be manipulated by a machine tended by a robot, wherein each pose specifies a position and an orientation of the workpiece with respect to the machine;
• generating, from each pose, a respective state node in a graph representing a state of the workpiece during the manipulation process;
• generating, for one or more state nodes, one or more grasp annotations that specify how the workpiece can be grasped by the robot in a state represented by the state node;
• generating, for each state node, one or more tasks including selecting, for each task, a respective grasp annotation that specifies how the robot will grasp the workpiece while performing the task;
• generating edges in the graph to connect state nodes in the grasp, each edge representing a respective task of tasks generated for the state nodes;
• selecting a path through the graph according to one or more task planning criteria;
• generating a behavior tree corresponding to the selected path, wherein leaves of the behavior tree are action nodes corresponding to the tasks represented by edges of the graph.