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US 12,214,502 B2Granted
Object manipulation
AssigneeHonda Motor Co., Ltd.
InventorsRianna Jitosho, Rana Soltani Zarrin
Life of this patent
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Abstract
A robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function which may be a cost function for finger sliding based on a penalty for a sliding distance, a change in desired normal direction, and a wrench error associated with sliding a robot finger, and generating an object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may execute the object trajectory and the grasp sequence via the robot appendage and actuators.
Claims17
A system for object manipulation, comprising:
- 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 when an initial grasp and a final grasp are given and a second type of grasp sequence planning when no final grasp is given,
- 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 a robot appendage including a robot finger and one or more actuators,
- wherein the optimized cost function includes a cost for reachability of contact points, a wrench error for realizing object trajectory, a contact force magnitude, a cost for transition between consecutive grasps, and a sliding cost term including a cost for sliding the robot finger based on a sliding distance, a change in a desired normal direction, and a wrench error associated with sliding the robot finger, and
- wherein the reward function is based on the cost for reachability of contact points, the wrench error for realizing object trajectory, the contact force magnitude, a transition penalty for adding or sliding the robot finger already at a desired location, a penalty for removing or sliding a robot finger which is not yet in contact with an object, a penalty for the sliding distance, the change in desired normal direction, and the wrench error associated with sliding the robot finger.
7 more elements
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 when an initial grasp and a final grasp are given and a second type of grasp sequence planning when no final grasp is given,
- 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,
- wherein the optimized cost function includes a cost for reachability of contact points, a wrench error for realizing object trajectory, a contact force magnitude, a cost for transition between consecutive grasps, and a sliding cost term including a cost for sliding the robot finger based on a sliding distance, a change in a desired normal direction, and a wrench error associated with sliding the robot finger, and
- wherein the reward function is based on the cost for reachability of contact points, the wrench error for realizing object trajectory, the contact force magnitude, a transition penalty for adding or sliding the robot finger already at a desired location, a penalty for removing or sliding a robot finger which is not yet in contact with an object, a penalty for the sliding distance, the change in desired normal direction, and the wrench error associated with sliding the robot finger.
5 more elements
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 when an initial grasp and a final grasp are given and a second type of grasp sequence planning when no final grasp is given,
- 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,
- wherein the optimized cost function includes a cost for reachability of contact points, a wrench error for realizing object trajectory, a contact force magnitude, a cost for transition between consecutive grasps, and a sliding cost term including a cost for sliding the robot finger based on a sliding distance, a change in a desired normal direction, and a wrench error associated with sliding the robot finger, and
- wherein the reward function is based on the cost for reachability of contact points, the wrench error for realizing object trajectory, the contact force magnitude, a transition penalty for adding or sliding the robot finger already at a desired location, a penalty for removing or sliding the robot finger which is not yet in contact with an object, a penalty for the sliding distance, the change in desired normal direction, and the wrench error associated with sliding the robot finger.
9 more elements
Dependencies are inferred from the claim text.