Provided is an operation command generation device configured to generate an operation command, which is a collection of jobs to be carried out by a process system including a robot based on a protocol chart including a process symbol representing a process to be carried out on a container containing a process subject, the operation command generation device including: a process job generation unit configured to generate, based on the process symbol, a job for causing the process system to carry out the process on the container at a work area; and a transfer job generation unit configured to generate, when the process represented by the process symbol is not a process to be carried out on the same container, a job to transfer the container from the work area to a retreat area after the process represented by the process symbol has been carried out.

Some implementations of this specification are directed generally to deep machine learning methods and apparatus related to predicting motion(s) (if any) that will occur to object(s) in an environment of a robot in response to particular movement of the robot in the environment. Some implementations are directed to training a deep neural network model to predict at least one transformation (if any), of an image of a robot's environment, that will occur as a result of implementing at least a portion of a particular movement of the robot in the environment. The trained deep neural network model may predict the transformation based on input that includes the image and a group of robot movement parameters that define the portion of the particular movement.

Robots have the capacity to perform a broad range of useful tasks, such as factory automation, cleaning, delivery, assistive care, environmental monitoring and entertainment. Enabling a robot to perform a new task in a new environment typically requires a large amount of new software to be written, often by a team of experts. It would be valuable if future technology could empower people, who may have limited or no understanding of software coding, to train robots to perform custom tasks. Some implementations of the present invention provide methods and systems that respond to users' corrective commands to generate and refine a policy for determining appropriate actions based on sensor-data input. Upon completion of learning, the system can generate control commands by deriving them from the sensory data. Using the learned control policy, the robot can behave autonomously.

Systems, methods and apparatus for automated vehicle wheel removal and replacement are provided. One system includes a computer system with applications for scheduling the replacement of tires for the vehicle. An electronically controlled lift device and robotic apparatus is configured for interaction with the computer system. The lift device mechanically adjusts arms for placement on lift points of vehicles. The robotic apparatus detects positioning of lug nut configuration for a wheel, removes lug nuts, and then removes the wheel from the wheel hub with gripping arms. The wheel and tire are then handed off to a separate tire changing machine. When a new tire is replaced the robotic apparatus then mounts the wheel to the original wheel hub, and then secures the lug nuts to the lug nut bolts.

To provide a machine teaching terminal, a machine, a program, and a safety confirmation method capable of ensuring the safety of a worker without providing an additional device or the like. A machine teaching terminal communicably connected to a robot so as to be used for teaching the robot within a work area of the robot includes a touch panel display configured to accept input performed by a worker, an input detection part configured to detect input to the touch panel display, an abnormality detection part configured to detect an abnormal state on the basis of the detection by the input detection part, and an abnormal signal transmission part configured to transmit, in the case where the abnormality detection part detects the abnormal state, a signal indicating the abnormal state to the robot.

A device for setting an operation of a robot unit where the device is adapted to create or modify a robot control program that is executed by a robot controller that controls the operation of the robot unit. The device includes a Central Processing Unit (CPU) adapted to execute a software program for creating or modifying the robot control program, wherein a user platform is adapted to be connected to the device for enabling input of information to the software program. The device has a platform independent Representational State Transfer communication protocol (REST client) and means for transferring a platform independent REST client to the user platform, which platform independent REST client enables the user platform to communicate with the software program.

Robotic task program synthesis embodiments are presented that generally synthesize a robotic task program based on received examples of repositioning tasks. In one implementation, the exemplary repositioning tasks are human demonstrations of object manipulation in an actual or displayed robot workspace. A domain specific language (DSL) designed for object repositioning tasks is employed for the robotic control program. In general, candidate robotic task programs are generated from the example tasks. Each candidate program includes instructions for causing the robot to reposition objects, and represents a different permutation of instructions consistent with the received example tasks. The candidate programs are ranked, and whenever the top ranking program accomplishes the repositioning specified in each example task, it is designated as the synthesized robotic task program.

Disclosed are systems and methods for detecting a graphic card that visually describes an operational mode of a rotatable interface component via a plurality of curves for rotationally-varying parameters, determining the operational mode that is visually described on the graphic card, and loading the operational mode to the rotatable interface component, where the operational mode specifies operations for a motor such that the motor generates torque on the interface component based on the curves for the rotationally-varying parameters that are shown on the graphic card.

Exemplary embodiments relate to user-assisted robotic control systems, user interfaces for remote control of robotic systems, vision systems in robotic control systems, and modular grippers for use by robotic systems. The systems, methods, apparatuses and computer-readable media instructions described interact with and control robotic systems, in particular pick and place systems using soft robotic actuators to grasp, move and release target objects.

A cloud computing-based server includes: a personalization code information receiving unit configured to receive personalization code information from a mobile device; a feedback data receiving unit configured to receive feedback data of the service from the mobile device, the smart device or the robot; a data receiving unit configured to receive, from an information processing center, standard customized information generated based on preset criteria depending on purpose and use of the service, a standard avatar program and a standard smart program; an optimization customized information generation unit configured to generate optimization customized information based on the personalization code information, the feedback data and the standard customized information; an optimization avatar program generation unit configured to generate an optimization avatar program to be executed on the mobile device based on the personalization code information, the feedback data and the standard avatar program; and an optimization smart program generation unit.