Method for controlling the operation of at least one machine (1), which is in particular configured to carry out pick-and-place or singulation tasks on objects (2), wherein the machine (1) comprises at least one functional device that comprises at least one functional element for carrying out at least one task, wherein the operation of the machine (1) is controlled on the basis of control information in order to carry out the at least one task, wherein the control information is generated on the basis of a plurality of task parameter types (APT) that relate to the operation of the machine (1) in order to carry out the task, wherein the task parameter types (APT) are stored on at least one data storage device (10) in a linked manner on the basis of predefined links specific to the task parameter types.

Techniques are described that determine motion of a robot's body that will maintain an end effector within a useable workspace when the end effector moves according to a predicted future trajectory. The techniques may include determining or otherwise obtaining the predicted future trajectory of the end effector and utilizing the predicted future trajectory to determine any motion of the body that is necessary to maintain the end effector within the useable workspace. In cases where no such motion of the body is necessary because the predicted future trajectory indicates the end effector will stay within the useable workspace without motion of the body, the body may remain stationary, thereby avoiding the drawbacks caused by unnecessary motion described above. Otherwise, the body of the robot can be moved while the end effector moves to ensure that the end effector stays within the useable workspace.

A computer-implemented method includes detecting, at a processor and by a plurality of associates, a mission to be performed by the plurality of associates; identifying the mission based on associated store information comprising an inventory status, sales data, and a set of predetermined rules; generating, by the processor, a queue of tasks to complete the mission based on priorities and dependencies of the tasks; determining a task for each associate whose profile defines best abilities matching a predetermined task dataset and the associated store information; assigning the queue of tasks to the plurality of the associates to complete the tasks; receiving, from each of the associates, a notification of a completion of an assigned task; verifying, by the processor, the completion of the assigned task; and determining, by the processor, completion of the mission when each task for the mission is verified to be completed.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for data-driven robotic control. One of the methods includes maintaining robot experience data; obtaining annotation data; training, on the annotation data, a reward model; generating task-specific training data for the particular task, comprising, for each experience in a second subset of the experiences in the robot experience data: processing the observation in the experience using the trained reward model to generate a reward prediction, and associating the reward prediction with the experience; and training a policy neural network on the task-specific training data for the particular task, wherein the policy neural network is configured to receive a network input comprising an observation and to generate a policy output that defines a control policy for a robot performing the particular task.

A robot task system includes: a robot; a transfer device configured to be driven to transfer a plurality of workpieces thereon by a specific distance at a time, the plurality of workpieces being placed within the specific distance; a driving management unit configured to manage a driving distance and a driving start timing of the transfer device for driving the transfer device each time; a task position generation unit configured to generate a plurality of task positions at the driving start timing managed by the driving management unit, the plurality of task positions being positions for the robot to execute a predetermined task on the plurality of workpieces; a task unit configured to update, according to the driving of the transfer device, the plurality of task positions generated by the task position generation unit and generate a task command to cause the robot to execute the predetermined task on the plurality of workpieces while following the plurality of workpieces; and a control unit configured to control the transfer device based on the driving distance and the driving start timing of the transfer device, and control the robot based on the task command generated by the task unit.

A control device includes a processor that calculates one or more predetermined command values for one or more robots to undergo synchronous control in predetermined control cycles, an output unit that outputs the one or more predetermined command values in each of the predetermined control cycles, and a generator that generates an output signal for a virtual robot. The virtual robot is virtually defined in relation to the synchronous control. The processor calculates the one or more predetermined command values using the output signal for the virtual robot generated by the generator.

A robot is provided. The robot includes a microphone, a camera, a communication interface including a circuit, a memory storing at least one instruction, and a processor, wherein the processor is configured to acquire a user voice through the microphone, identify a task corresponding to the user voice, determine whether the robot can perform the identified task, and control the communication interface to transmit information on the identified task to an external robot based on the determination result.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating control instructions for operating a robot. One of the methods includes generating an interactive user interface that illustrates an object to be manipulated by a robot by using an end effector; receiving, within the user interface, first user input data indicating a workcell location; computing a surface normal of a surface in the workcell corresponding to the workcell location; presenting, within the user interface, a graphical representation of the surface normal corresponding to the workcell location; receiving, within the user interface, second user input data selecting the workcell location; and generating pose data for the robot using the computed surface normal and the workcell location.

Provided are systems and methods for maximizing saturation of two different sets of actors performing different sets of dependent operations at different rates over different but overlapping periods of time in a non-conflicting manner. The systems and methods may include transferring a first set of ordered items from item storage to item cache locations at a first rate during a first period of time, and fulfilling orders at a faster second rate over a later second period of time by picking items from a first set of the item cache locations at the second rate, and by replacing items at a non-overlapping second set of the item cache locations at the first rate. The transferring is commenced before the picking to create a buffer that allows a first set of actors, operating at the first rate, to continually provide the dependencies needed for a second set of actors to operate at the faster second rate without conflict and with each set of actors operating at their respective maximum rates.

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.