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US 12,097,614 B2Granted
Object manipulation
AssigneeHonda Motor Co., Ltd.
InventorsRana Soltani Zarrin, Katsu Yamane
Life of this patent
Estimating the term…
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 based on an inverse kinematic (IK) error, a difference between an estimated required wrench and an actual wrench, and a grasp efficiency, and generating a reference 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 implement the reference object trajectory and the grasp sequence via the robot appendage and actuators.
Claims20
A system for object manipulation, comprising:
- a planner performing:
- object path planning to determine a first object pose, a second object pose, and an Nth object pose;
- object trajectory optimization by:
- assigning a set of timestamps to the first object pose, the second object pose, and the Nth object pose;
- optimizing a cost function based on a first factor indicative of an inverse kinematic (IK) error, a second factor indicative of a difference between an estimated required wrench and an actual wrench, and a third factor indicative of a grasp efficiency;
- and
- generating a reference object trajectory based on the optimized cost function, wherein
- the planner uses a path length of a path of a planned object trajectory as the cost function to be minimized and the reference object trajectory is based on the planned object trajectory, and
- the path of the planned object trajectory includes a plurality of waypoints representing the first object pose, the second object pose, and the Nth object pose at the assigned corresponding timestamps such that the plurality of waypoints is traversed from a start configuration to a goal configuration with a minimum path length of the path;
- and
- grasp sequence planning 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 the optimized cost function and minimizing added contact points and/or removed contact points,
- wherein the second type of grasp sequence planning is based on a deep reinforcement learning (DRL) policy trained based on a reward function, wherein the reward function is based on a first term indicative of the IK error, a second term indicative of the difference between the estimated required wrench and the actual wrench, and a third term indicative of the grasp efficiency;
- and
- a controller, implementing the reference object trajectory and the grasp sequence via a robot appendage and one or more actuators.
12 more elements
A method for object manipulation, comprising:
- performing object path planning to determine a first object pose, a second object pose, and an Nth object pose;
- performing object trajectory optimization by:
- assigning a set of timestamps to the first object pose, the second object pose, and the Nth object pose;
- optimizing a cost function based on a first factor indicative of an inverse kinematic (IK) error, a second factor indicative of a difference between an estimated required wrench and an actual wrench, and a third factor indicative of a grasp efficiency;
- and
- generating a reference object trajectory based on the optimized cost function, wherein
- a path length of a path of a planned object trajectory is the cost function to be minimized and the reference object trajectory is based on the planned object trajectory, and
- the path of the planned object trajectory includes a plurality of waypoints representing the first object pose, the second object pose, and the Nth object pose at the assigned corresponding timestamps such that the plurality of waypoints is traversed from a start configuration to a goal configuration with a minimum path length of the path;
- performing grasp sequence planning generating a grasp sequence based on 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 the optimized cost function and minimizing added contact points and/or removed contact points,
- wherein the second type of grasp sequence planning is based on a deep reinforcement learning (DRL) policy trained based on a reward function, wherein the reward function is based on a first term indicative of the IK error, a second term indicative of the difference between the estimated required wrench and the actual wrench, and a third term indicative of the grasp efficiency;
- and
- implementing the reference object trajectory and the grasp sequence via a robot appendage and one or more actuators.
10 more elements
A robot for object manipulation, comprising:
- a robot appendage;
- one or more actuators configured to drive one or more joints of the robot appendage;
- a planner performing:
- object path planning to determine a first object pose, a second object pose, and an Nth object pose;
- object trajectory optimization by:
- assigning a set of timestamps to the first object pose, the second object pose, and the Nth object pose;
- optimizing a cost function based on a first factor indicative of an inverse kinematic (IK) error, a second factor indicative of a difference between an estimated required wrench and an actual wrench, and a third factor indicative of a grasp efficiency;
- and
- generating a reference object trajectory based on the optimized cost function, wherein
- the planner uses a path length of a path of a planned object trajectory as the cost function to be minimized and the reference object trajectory is based on the planned object trajectory, and
- the path of the planned object trajectory includes a plurality of waypoints representing the first object pose, the second object pose, and the Nth object pose at the assigned corresponding timestamps such that the plurality of waypoints is traversed from a start configuration to a goal configuration with a minimum path length of the path;
- and
- grasp sequence planning 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 the optimized cost function and minimizing added contact points and/or removed contact points,
- wherein the second type of grasp sequence planning is based on a deep reinforcement learning (DRL) policy trained based on a reward function, wherein the reward function is based on a first term indicative of the IK error, a second term indicative of the difference between the estimated required wrench and the actual wrench, and a third term indicative of the grasp efficiency;
- and
- a controller, implementing the reference object trajectory and the grasp sequence via the robot appendage and one or more of the actuators.
14 more elements
Dependencies are inferred from the claim text.