According to one embodiment, a robot hand grips an object. The robot hand includes first and second communicators, and a hand controller. The first communicator communicates grip data with a first device. The grip data is related to a gripping operation. The second communicator communicates a start notification and an end notification with a second device. The second communicator can communicate faster than the first communicator. The start notification is for starting the gripping operation. The end notification indicates an end of the gripping operation. The hand controller controls the gripping operation. In response to the start notification input to the second communicator, the hand controller starts the gripping operation. In response to the end of the gripping operation, the hand controller performs outputting the end notification, and outputting at least one of a result of the gripping operation or a state of the robot hand.

A vacuum adsorption system includes a cylinder including a cylinder block, a piston, and a piston rod mounted in the cylinder block, and a vacuum pressure control device controlling a vacuum pressure in an inner cavity of the cylinder block. The piston rod has a vacuum suction hole communicating with the inner cavity. The vacuum pressure in the inner cavity is controlled so that a contact force applied by the piston rod on an object adsorbed by the vacuum suction hole of the piston rod is less than or equal to a predetermined contact force.

An object manipulation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: calculate, based on an image in which one or more objects to be grasped are contained, an evaluation value of a first behavior manner of grasping the one or more objects; generate information representing a second behavior manner based on the image and a plurality of evaluation values of the first behavior manner; and control actuation of grasping the object to be grasped in accordance with the information being generated.

An apparatus and a method for planning contact-interaction trajectories are provided. The apparatus is a robot that accepts contact interactions between the robot and the environment. The robot stores a dynamic model representing geometric, dynamic, and frictional properties of the robot and the environment, and a relaxed contact model to representing dynamic interactions between the robot and the object via virtual forces. The robot further determines, iteratively until a termination condition is met, a trajectory, associated control commands for controlling the robot, and virtual stiffness values by performing optimization reducing stiffness of the virtual force and minimizing a difference between the target pose of the object and a final pose of the object moved from the initial pose. Further, an actuator moves a robot arm of the robot according to the trajectory and the associated control commands.

In current applications of autonomous machines in industrial settings, the environment, in particular the devices and systems with which the machine interacts, is known such that the autonomous machine can operate in the particular environment successfully. Thus, current approaches to automating tasks within varying environments, for instance complex environments having uncertainties, lack capabilities and efficiencies. In an example aspect, a method for operating an autonomous machine within a physical environment includes detecting an object within the physical environment. The autonomous machine can determine and perform a principle of operation associated with a detected subcomponent of the object, so as to complete a task that requires that the autonomous machine interacts with the object. In some cases, the autonomous machine has not previously encountered the object.

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.

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.

In a robot (industrial robot) system, a robot holds a workpiece by pinching the workpiece between movable claws. A controller, which controls the robot, includes a host controller that controls the robot to perform a positioning operation for positioning the hand to a grip position and a gripping operation for displacing each of the movable claws toward each other at the grip position. In the controller, a machine learning device acquires stop reference data set for gripping of the workpiece, distance data indicating a distance between each of the movable claws of the hand positioned at the grip position and the workpiece, and comparison data indicating a deformation amount of the workpiece before and after the gripping operation. The machine learning device performs machine learning using such acquired data, resulting in constructing a model used for setting an operation mode of the gripping operation.

Robotic gripping devices and methods for performing a picking operation. The methods described herein may involve positioning a gripping device with respect to an item to be grasped and then executing a first picking operation using the gripping device to obtain a grasp on the item. The methods may then involve executing at least two of a force detection procedure to detect a force applied to a portion of the gripping device, a grasping space detection procedure to detect an item in grasping range of the gripping device, a pressure detection procedure configured to detect pressure in an airflow path, and an item load detection procedure to detect force in a mechanical load path of the gripping device.