Machine learning methods and apparatus for robotic manipulation and that utilize multi-task domain adaptation

A robot, comprising:

• an end effector;
• actuators;
• a vision component;
• memory;
• one or more processors executing instructions stored in the memory to:
• identify an image captured by the vision component;
• identify a target object captured by the image;
• generate a target object input that denotes position of the target object in the image;
• generate a candidate end effector motion vector defining motion to move the end effector from the current pose to an additional pose;
• identify a current image captured by the vision component, the current image capturing the grasping end effector and at least the target object;
• process the current image, the candidate end effector motion vector, and the target object input using a trained instance grasping model;
• generate, based on the processing, a prediction of successful grasp of the target object with application of the motion defined by the candidate end effector motion vector;
• generate an end effector command based on the prediction of successful grasp of the target object;
• and
• provide the end effector command to one or more of the actuators of the robot.