Towards Robotic Assembly by Predicting Robust, Precise and Task-oriented Grasps

Understand

Robust task-oriented grasp planning is vital for autonomous robotic precision assembly tasks.

• Knowledge of the objects' geometry and preconditions of the target task should be incorporated when determining the proper grasp to execute.
• However, several factors contribute to the challenges of realizing these grasps such as noise when controlling the robot, unknown object properties, and difficulties modeling complex object-object interactions.
• We propose a method that decomposes this problem and optimizes for grasp robustness, precision, and task performance by learning three cascaded networks.