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.

The disclosure provides a gripping position determination device, a gripping position determination system, a gripping position determination method, and a recording medium. The gripping position determination device for a robot hand having a plurality of multi joint fingers includes: a frictional force distribution calculation part estimating, from a predictive control of a gripping force when an object is gripped by at least two fingers, a frictional force between one of the gripping fingers and the object, and calculates a frictional force distribution where grapping of the object is possible on a surface of the object based on a value related to a frictional force calculated by using the estimated frictional force; a grippable region selection part selecting, from the frictional force distribution, at least one grippable region; and a gripping position calculation part calculating, from the selected grippable region, a gripping position where stable gripping of the object is possible.

A method and system are herein disclosed wherein a robot handles objects that are large, unwieldy, highly-deformable, or otherwise difficult to contain and carry. The robot is operated to navigate an environment and detect and classify objects using a sensing system. The robot determines the type, size and location of objects and classifies the objects based on detected attributes. Grabber pad arms and grabber pads move other objects out of the way and move the target object onto the shovel to be carried. The robot maneuvers objects into and out of a containment area comprising the shovel and grabber pad arms following a process optimized for the type of object to be transported. Large, unwieldy, highly deformable, or otherwise difficult to maneuver objects may be managed by the method disclosed herein.

In variants, a method for robot control can include: receiving sensor data of a scene, modeling the physical objects within the scene, determining a set of potential grasp configurations for grasping a physical object within the scene, determining a reach behavior based on the potential grasp configuration, determining a trajectory for the reach behavior, and grasping the object using the trajectory.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

A first method comprising: predicting a scene of an environment using a model of the environment and based on a first scene of the environment obtained from sensors observing scenes of the environment; comparing the predicted scene with an observed scene from the sensors; and performing an action based on differences determined between the predicted scene and the observed scene. A second method comprising applying a vibration stimuli on an object via a computer-controlled component; obtaining a plurality of images depicting the object from a same viewpoint, captured during the application of the vibration stimuli. The second method further comprising comparing the plurality of images to detect changes occurring in response to the application of the vibration stimuli, which changes are attributed to a change of a location of a boundary of the object; and determining the boundary of the object based on the comparison.

Methods and apparatus for determining a grasp strategy to grasp an object with a gripper of a robotic device are described. The method comprises generating a set of grasp candidates to grasp a target object, wherein each of the grasp candidates includes information about a gripper placement relative to the target object, determining, for each of the grasp candidates in the set, a grasp quality, wherein the grasp quality is determined using a physical-interaction model including one or more forces between the target object and the gripper located at the gripper placement for the respective grasp candidate, selecting, based at least in part on the determined grasp qualities, one of the grasp candidates, and controlling the robotic device to attempt to grasp the target object using the selected grasp candidate.

A robotic grasp generation technique for part picking applications. Part and gripper geometry are provided as inputs, typically from CAD files. Gripper kinematics are also defined as an input. A set of candidate grasps is provided using any known preliminary grasp generation tool. A point model of the part and a model of the gripper contact surfaces with a clearance margin are used in an optimization computation applied to each of the candidate grasps, resulting in an adjusted grasp database. The adjusted grasps optimize grasp quality using a virtual gripper surface, which positions the actual gripper surface a small distance away from the part. A signed distance field calculation is then performed on each of the adjusted grasps, and those with any collision between the gripper and the part are discarded. The resulting grasp database includes high quality collision-free grasps for use in a robotic part pick-and-place operation.

A method of teaching a robot includes: a swinging step of causing a hand to swing about a predetermined pivot, which is on an axis perpendicular to an optical axis of a sensor beam, to scan a target in a horizontal direction of the sensor beam; a determining step of determining whether or not the target has coincided with a position along a central axis of the hand in its longitudinal direction based on a detection signal of a mapping sensor, the detection signal having changed owing to the swinging of the hand; and a shifting step of, if it is determined in the determining step that the target has not coincided with the position, calculating an offset amount of the hand based on the detection signal of the mapping sensor, the detection signal having changed owing to the swinging of the hand, and causing the hand to shift to either right or left along the optical axis of the sensor beam in accordance with the calculated offset amount.

This disclosure relates generally to robotic network, and more particularly to a method and system for hierarchical decomposition of tasks and task planning in a robotic network. While a centralized system is used for action planning in a robotic network, any communication network issues can adversely affect working of the robotic network. Further, hardcoding one or more specific tasks to a robot restricts use of the robots irrespective of capabilities of the robots. The robotic agent decomposes a goal assigned to the robot to multiple sub-goals, and for each sub-goal, identifies one or more tasks to be executed/performed by the robot. An action plan is generated based on all such tasks identified, and the robot executes the action plan, in response to the goal assigned to the robot.