A robot system includes a robot, a vision sensor, a controller, and an input unit. The vision sensor configured to measure a feature point and obtain a measured coordinate value. The controller configured to control the robot. The input unit configured to receive an input from a user toward the controller. The controller obtains, via the input unit, setting information data on a determination point which is different from the feature point. The robot system uses a coordinate value of the determination point and the measured coordinate value, and determines whether the robot is taking a target position and orientation.

In some embodiments, correcting an alignment error between an end effector of a tool associated with a slave and a master actuator associated with a master in a robotic system involves receiving at the master, master actuator orientation signals (R.sub.MCURR) representing the orientation of the master actuator relative to a master reference frame and generating end effector orientation signals (R.sub.EENEW) representing the end effector orientation relative to a slave reference frame, producing control signals based on the end effector orientation signals, receiving an enablement signal for selectively enabling the control signals to be transmitted from the master to the slave, responsive to a transition of the enablement signal from not active state to active state, computing the master-slave misalignment signals (R.sub.) as a difference between the master actuator orientation signals (R.sub.MCURR) and the end effector orientation signals (R.sub.EENEW), and adjusting the master-slave misalignment signals (R.sub.) to reduce the alignment difference.

A method comprises receiving a surgical instrument into engagement with a grip actuator of a teleoperational activation system. The surgical instrument includes movable jaws, and the surgical instrument is received in a prearranged gripping configuration with the jaws gripping a surgical accessory. The method includes generating a first control signal for manipulating the surgical instrument while maintaining the surgical instrument in the prearranged gripping configuration. The method further includes generating a second control signal for manipulating the surgical instrument to move from the prearranged gripping configuration to a second configuration.

A system and method for providing proxy picking of non-fungible goods within an automated storage and retrieval system is provided, which repurposes one or more automated mobile robots operating within the automated inventory management system to perform a plurality of tasks across multiple different areas of an automated store. The proxy picking system and method are configured to pick individually identified non-fungible goods according to a customer selection on an ordering screen based on measured attributes and images of the goods, the attributes selected by the customer.

A method for manipulating a deformable object includes determining respective 3D position of one or more markers on the deformable object held by a robotic manipulator; determining a deformation model of the deformable object by mapping movement of the robotic manipulator and movement of one or more markers; and controlling the robotic manipulator based on the determined deformation model to manipulate the deformable object so as to move the one or more markers into respective target position.

A method and system for piece-picking or piece put-away within a logistics facility. The system includes a central server and at least one mobile manipulation robot. The central server is configured to communicate with the robots to send and receive piece-picking data which includes a unique identification for each piece to be picked, a location within the logistics facility of the pieces to be picked, and a route for the robot to take within the logistics facility. The robots can then autonomously navigate and position themselves within the logistics facility by recognition of landmarks by at least one of a plurality of sensors. The sensors also provide signals related to detection, identification, and location of a piece to be picked or put-away, and processors on the robots analyze the sensor information to generate movements of a unique articulated arm and end effector on the robot to pick or put-away the piece.

A robot setting apparatus includes a positioning unit that adjusts a position and an attitude of a workpiece model, and a grip position specifying unit that specifies a grip position at which the workpiece model is gripped by an end effector for at least one fundamental direction image in a state of displaying at least three height images in which the workpiece model positioned by the positioning unit on the virtual three-dimensional space is viewed from respective axis directions of three axes orthogonal to each other on the display unit as fundamental direction images.

A control device includes a control section configured to control a robot on the basis of first information concerning positions and sequential numbers for drawing a linear object around a first object. The first information is received by a receiving section and displayed on a display section.

An unmanned ground vehicle includes a main body, a drive system supported by the main body, a manipulator arm pivotally coupled to the main body, and a sensor module. The drive system includes right and left driven track assemblies mounted on right and left sides of the main body. The manipulator arm includes a first link coupled to the main body, an elbow coupled to the first link, and a second link coupled to the elbow. The elbow is configured to rotate independently of the first and second links. The sensor module is mounted on the elbow.

Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular taskand the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.