According to one embodiment, a holding mechanism includes a plurality of links, a first elastic body, and a second elastic body. The plurality of links are rotatably connected to one another at ends of the links. The first elastic body is disposed along the links. The second elastic body is interposed between adjacent links of the links so as to be along the first elastic body.

Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

A robot system for processing an object to be packaged as a product, a packaging method, and a method of processing the same are provided. The object has multiple surfaces, and multiple e-package information tags are provided on the surfaces of the object for storing information of the product. Each surface is provided with one of the e-package information tags. The information of the product includes information of a location, an orientation and physical features of the object. In operation, the robot system controls a sensing device to detect and capture one of the e-package information tags on the object to obtain a captured image, and processes the captured image to obtain the information of the product. Based on the information of the product, the robot system controls a robotic grasping device to perform a robotic manipulation for handling the object.

A method for operating a robotic system including determining an initial pose of a target object based on imaging data, wherein the initial pose is for estimating a resting orientation of the target object in a pickup area; calculating a confidence measure associated with the initial pose, wherein the confidence measure is for representing a likelihood of the initial pose being correct; and calculating a motion plan according to the confidence measure, the motion plan for executing a task based on picking up the target object from a start location, transferring the target object to a task location, and scanning one or more object identifiers between the start location and the task location.

A method for operating a robotic system including determining an initial pose of a target object based on imaging data, wherein the initial pose is for estimating a resting orientation of the target object in a pickup area; calculating a confidence measure associated with the initial pose, wherein the confidence measure is for representing a likelihood of the initial pose being correct; and calculating a motion plan according to the confidence measure, the motion plan for executing a task based on picking up the target object from a start location, transferring the target object to a task location, and scanning one or more object identifiers between the start location and the task location.

In at least one embodiment, under the control of a robotic control system, a gripper on a robot is positioned to grasp a 3-dimensional object. In at least one embodiment, the relative position of the object and the gripper is determined, at least in part, by using a camera mounted on the gripper.

Techniques described herein relate to generating a posteriori knowledge about where objects are typically located within environments to improve object location. In various implementations, output from vision sensor(s) of a robot may include visual frame(s) that capture at least a portion of an environment in which a robot operates/will operate. The visual frame(s) may be applied as input across a machine learning model to generate output that identifies potential location(s) of an object of interest. The robot's position/pose may be altered based on the output to relocate one or more of the vision sensors. One or more subsequent visual frames that capture at least a not-previously-captured portion of the environment may be applied as input across the machine learning model to generate subsequent output identifying the object of interest. The robot may perform task(s) that relate to the object of interest.

A method for operating a robotic system including determining an initial pose of a target object based on imaging data, wherein the initial pose is for estimating a resting orientation of the target object in a pickup area; calculating a confidence measure associated with the initial pose, wherein the confidence measure is for representing a likelihood of the initial pose being correct; and calculating a motion plan according to the confidence measure, the motion plan for executing a task based on picking up the target object from a start location, transferring the target object to a task location, and scanning one or more object identifiers between the start location and the task location.

A method for operating a robotic system including determining an initial pose of a target object based on imaging data, wherein the initial pose is for estimating a resting orientation of the target object in a pickup area; calculating a confidence measure associated with the initial pose, wherein the confidence measure is for representing a likelihood of the initial pose being correct; and calculating a motion plan according to the confidence measure, the motion plan for executing a task based on picking up the target object from a start location, transferring the target object to a task location, and scanning one or more object identifiers between the start location and the task location.

A robot includes: a hand; and a control unit that operates the hand, in which the control unit generates three-dimensional point group information for a partial image forming a captured image obtained by an imaging unit, and causes the hand to hold an object included in the partial image.