Object manipulation

A method for object manipulation, comprising:

• performing object trajectory planning through object path planning and object trajectory optimization;
• performing grasp sequence planning by generating a grasp sequence by a first type of grasp sequence planning or a second type of grasp sequence planning,
• wherein the first type of grasp sequence planning is based on an optimized cost function,
• wherein the second type of grasp sequence planning is based on a deep reinforcement learning (DRL) policy trained based on a reward function associated with the optimized cost function;
• and
• executing the object trajectory and the grasp sequence via a robot appendage including a robot finger and one or more actuators.

A robot for object manipulation, comprising:

• a robot appendage including a robot finger;
• one or more actuators configured to drive one or more joints of the robot appendage;
• a planner performing:
• object trajectory planning through object path planning and object trajectory optimization;
• and
• grasp sequence planning by generating a grasp sequence by a first type of grasp sequence planning or a second type of grasp sequence planning,
• wherein the first type of grasp sequence planning is based on an optimized cost function for the planner,
• wherein the second type of grasp sequence planning is based on a deep reinforcement learning (DRL) policy trained based on a reward function for the planner associated with the optimized cost function;
• and
• a controller, executing the object trajectory and the grasp sequence via the robot appendage and one or more of the actuators.