A robot teleoperation control device includes a first acquisition unit that acquires operator state information of a state of an operator who operates a robot, an intention estimation unit that estimates an intention of the operator to cause the robot to perform a motion on the basis of the operator state information, a second acquisition unit that acquires at least one of geometric information and dynamic information of the object, an operation method determination unit that determines a method of operating the object based on the estimated motion intention of the operator, and a control amount determination unit that determines a method of operating the robot and force during operation from the information acquired by the second acquisition unit and information determined by the operation method determination unit and reflects the result in a control instruction.

An object handling control device includes one or more processors configured to acquire at least object information and status information representing an initial position and a destination of an object; set, when a grasper grasping the object moves from the initial position to the destination, a first region, a second region, and a third region in accordance with the object information and the status information; and calculate a moving route along which the object is moved from the initial position to the destination with reference to the first region, the second region, and the third region.

An annotation device includes an image-capturing device, a robot, a control unit, a designation unit, a coordinate processing unit, and a storage unit. The control unit controls the robot so as to acquire a learning image of a plurality of objects, each having a different positional relationship with the image-capturing devices. Furthermore, the storage unit converts a position of the object in a robot coordinate system into a position of the object in an image coordinate system at the time of image-capturing or a position of the object in a sensor coordinate system, and stores the position thus converted together with the learning image.

The present disclosure provides a stair climbing gait planning method and an apparatus and a robot using the same. The method includes: obtaining first visual measurement data through a visual sensor of the robot; converting the first visual measurement data to second visual measurement data; and performing a staged gait planning on a process of the robot to climb the staircase based on the second visual measurement data. Through the method, the visual measurement data is used as a reference to perform the staged gait planning on the process of the robot to climb the staircase, which greatly improves the adaptability of the robot in the complex scene of stair climbing.

Disclosed are various embodiments of a three-dimensional perception and object manipulation robot gripper configured for connection to and operation in conjunction with a robot arm. In some embodiments, the gripper comprises a palm, a plurality of motors or actuators operably connected to the palm, a mechanical manipulation system operably connected to the palm, a plurality of fingers operably connected to the motors or actuators and configured to manipulate one or more objects located within a workspace or target volume that can be accessed by the fingers. A depth camera system is also operably connected to the palm. One or more computing devices are operably connected to the depth camera and are configured and programmed to process images provided by the depth camera system to determine the location and orientation of the one or more objects within a workspace, and in accordance therewith, provide as outputs therefrom control signals or instructions configured to be employed by the motors or actuators to control movement and operation of the plurality of fingers so as to permit the fingers to manipulate the one or more objects located within the workspace or target volume. The gripper can also be configured to vary controllably at least one of a force, a torque, a stiffness, and a compliance applied by one or more of the plurality of fingers to the one or more objects.

Various surgical systems are disclosed. A surgical system can include a surgical robot and a surgical hub. The surgical robot can include a control unit in signal communication with a control console and a robotic tool. The surgical hub can include a display. The surgical hub can be in signal communication with the control unit. A facility can include a plurality of surgical hubs that communicate data from the surgical robots to a primary server. To alleviate bandwidth competition among the surgical hubs, the surgical hubs can include prioritization protocols for collecting, storing, and/or communicating data to the primary server.

Training and/or using a recurrent neural network model for visual servoing of an end effector of a robot. In visual servoing, the model can be utilized to generate, at each of a plurality of time steps, an action prediction that represents a prediction of how the end effector should be moved to cause the end effector to move toward a target object. The model can be viewpoint invariant in that it can be utilized across a variety of robots having vision components at a variety of viewpoints and/or can be utilized for a single robot even when a viewpoint, of a vision component of the robot, is drastically altered. Moreover, the model can be trained based on a large quantity of simulated data that is based on simulator(s) performing simulated episode(s) in view of the model. One or more portions of the model can be further trained based on a relatively smaller quantity of real training data.

A sensor-based item transport system, and a method therefore are described. The system includes, for example, a cart station, within a restricted area including a plurality of automated drive. A light curtain is adjacent to the cart station. A first sensor and a second sensor are spaced apart from the first sensor within the cart station. A first mode associated with the light curtain is maintained causing an alarm system associated with the light curtain to remain armed. The first mode is caused to change to a second mode associated with the light curtain, the second mode causing the alarm system to be muted, based at least in part on the identity of the cart. The identity is determined based at least in part on one or more signals received from the first sensor and the second sensor.

Described herein are assistant robots that anticipate needs of one or more people (or animals). The assistant robots may recognize a current activity, knowledge of the person's routines, and contextual information. As such, the assistant robots can provide or offer to provide appropriate robotic assistance. The assistant robots can learn users' habits or be provided with knowledge regarding humans in its environment. The assistant robots develop a schedule and contextual understanding of the persons' behavior and needs. The assistant robots may interact, understand, and communicate with people before, during, or after providing assistance. The robot can combine gesture, clothing, emotional aspect, time, pose recognition, action recognition, and other observational data to understand people's medical condition, current activity, and future intended activities and intents.

In at least one embodiment, a system determines a set of possible grasp poses that allow a robot to successfully grasp an object by generating a set of potential grasp poses, and then evaluating the performance of each potential grasp pose. In at least one embodiment, the system performs a refinement operation on the grasp poses, and based on an evaluation of the poses, creates an improved set of possible grasps for the object.