A surgical robotic system includes a surgical console having a display and a user input device configured to generate a user input and a surgical robotic arm, which includes a surgical instrument configured to treat tissue and being actuatable in response to the user input and a video camera configured to capture video data that is displayed on the display. The system also includes a control tower coupled to the surgical console and the surgical robotic arm. The control tower is configured to process the user input to control the surgical instrument and to record the user input as input data; communicate the input data and the video data to at least one machine learning system configured to generate a surgical process evaluator; and execute the surgical process evaluator to determine whether the surgical instrument is properly positioned relative to the tissue.

A method of setting a target force upper limit for a robot gripping an object with a gripping unit and operating by force control to bring an acting force close to a target force, includes gripping the object with the gripping unit, performing a pressing operation to press the object gripped by the gripping unit against a contact surface by the force control, performing a pressing force acquisition operation to acquire the force acting on the gripping unit during the pressing operation as a pressing force, repeating a setting change operation to increase the target force, the pressing operation, and the pressing force acquisition operation until a state in which the pressing force is not equal to or larger than the target force appears, and setting a target force upper limit based on the pressing force acquired in the pressing force acquisition operation at a time when the state appears.

A robot control method, and associated robot controllers and robots operating with such methods and controllers, providing real-time vibration suppression. The control method involves learning to support real-time, vibration-suppressing control. The method uses state-of-the-art machine learning techniques in conjunction with a differentiable dynamics simulator to yield fast and accurate vibration suppression. Vibration suppression using offline simulation approaches that can be computationally expensive may be used to create training data for the controller, which may be provide by a variety of neural network configurations. In other cases, sensory feedback from sensors onboard the robot being controlled can be used to provide training data to account for wear of the robot's components.

According to one embodiment, a processing system teaches an operation to a robot. The robot includes a detector including detection elements arranged along first and second directions, and a manipulator to which the detector is mounted. The processing system performs position teaching processing. The position teaching processing includes causing the detector to perform a probe of a weld portion of a joined body. The probe includes a transmission of an ultrasonic wave and a detection of a reflected wave. The position teaching processing includes calculating a center position of the weld portion in a first plane based on first intensity data of an intensity of the reflected wave, setting a teaching point of the robot based on a first position of the detector, and moving the detector along the first plane to a second position, and setting the teaching point based on the second position.

A smart change evaluation engine analyzes an existing RPA system and process flow and an updated RPA system and process flow and evaluates identified changes in the process including changes to components, elements, controls. The smart change evaluation engine may output an appropriate change estimate value corresponding to an amount of change and/or effort to modify the RPA process flow. The smart change evaluation engine may analyze the recorded changes and assigns a rule-based value for every component in the process flow for the identified changes. A scoring algorithm is used to generate a change estimate value based on the analysis of the pre-change recordings, the post-change recordings, and from the data values captured from the actual robotic automated system, by doing dual checks resulting in best calculated estimates. The scoring value derived from the scoring algorithm by the smart change evaluation engine is used as an input for evaluating changes for robotics process automation that may be required to support further development of services and/or applications provided by an enterprise organization.

A method for controlling a serving robot is provided. The method includes the steps of: determining a first time to request payment from a customer on the basis of identification information on the customer and information on an order of the customer acquired with respect to the customer; acquiring information on eating status of the customer at a second time associated with the first time; and adjusting at least one of the first time and the second time on the basis of at least one of the information on the eating status of the customer and information on an additional order of the customer additionally acquired with respect to the customer.

A method for computing joint torques applied by actuators to perform a control of a movement of a robot arm having several degrees of freedom is provided. The method includes the act of providing, by a trajectory generator, trajectory vectors specifying a desired trajectory of the robot arm for each degree of freedom. The trajectory vectors are mapped to corresponding latent representation vectors that capture inherent properties of the robot arm using basis functions with trained parameters. The latent representation vectors are multiplied with trained core tensors to compute the joint torques for each degree of freedom.

A method of configuring robot fleets with additive manufacturing capabilities includes receiving a request for a robotic fleet to perform a job and determining a job definition data structure based on the request. The job definition data structure defines a set of tasks to be performed in furtherance of the job. The method includes determining a provisioning configuration for each additive manufacturing system based on the task to which the additive manufacturing system is assigned, the set of 3D printing requirements, the printing instructions, and the status of the additive manufacturing system. The method includes provisioning the additive manufacturing system based on the provisioning configuration and a set of additive manufacturing system provisioning rules that are accessible to an intelligence layer to ensure that provisioned systems comply with the provisioning rules. The method includes deploying the robotic fleet based on the robotic fleet configuration data structure to perform the job.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the determination of control policies for robots through the performance of simulations of robots and real-world context to determine control policy parameters.

A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.