Abstract: A robot system causing a robot to operate in response to a handling force, wherein a position of the robot can be adjusted with higher accuracy. In one aspect of the present disclosure, a robot system includes a robot having a handle, a force sensor configured to detect a handling force applied to the handle, and an inching motion execution section configured to execute an inching motion of causing the robot to move by a movement amount determined in response to the handling force detected by the force sensor.

Abstract: A mother-child robot cooperative work system and a work method thereof include a mother robot and a charging base0, in which the mother robot is provided with a control unit and a work unit. The system also includes child robot, communicatively coupled to the mother robot. The mother robot performs cleaning for a work area under the control of the control unit, and recognizes cleanable area and assisted cleaning area in a cleaning process. After cleaning work in the cleanable area is completed, the control unit in the mother robot controls the child robot to cooperatively complete the cleaning work in the assisted cleaning area. The mother robot is provided with a child robot pose sensing unit. The unit inputs child robot pose information to the control unit; and the control unit controls the child robot to act as indicated.

Abstract: A method and system for programming picking and placing of a workpiece is provided. Embodiments may include associating a workpiece with an end effector that is attached to a robot and scanning the workpiece while the workpiece is associated with the end effector. Embodiments may also include determining a pose of the workpiece relative to the robot, based upon, at least in part, the scanning.

Abstract: Systems, devices, articles, and methods, described in greater detail herein, including robotic systems which include at least one rangefinder, at least one manipulator, and at least one processor in communication with the at least one rangefinder, and methods of operation of the same. The at least one processor obtains rangefinder pose information which represents, at least, the at least one manipulator in a plurality of poses. The at least one processor obtains manipulator pose information, optimizes a model of mismatch between the rangefinder pose information and the manipulator pose information, wherein the model of mismatch includes a plurality of parameters, and updates at least one processor readable storage device with the plurality of parameters based at least in part on the optimization.

Abstract: The application relates to a smart cabinet. The smart cabinet includes a cabinet body, a moving module, a controlling module and an assisting module. The moving module is positioned in the cabinet body; the controlling module is connected to the moving module. The moving module includes a base, a guiding wheel group disposed on the base, a plurality of drivers pivotally connected with the guiding wheel group, and a manipulator disposed on the base. The controlling module includes an input control unit, a guiding rope, and a pulley group. The input control module is electrically connected with the drivers and the manipulator. The pulley group includes a plurality of pulleys defining a movement range for the moving module. The assisting module includes a rope retractor and a sensor electrically coupled to the rope retractor. Two ends of the guiding rope are coupled to the rope retractor.

Abstract: According to an aspect, there is provided a teaching device on which portable terminal equipment is mounted, the teaching device being used to teach operation to a robot. The teaching device includes a case section including a mounting surface on which the portable terminal equipment is mounted. The case section includes a first member including a stop switch for stopping the operation of the robot and configured to restrict the portable terminal equipment's moving in a first direction extending along the mounting surface and a second member provided to be capable of moving in the first direction relatively to the first member and configured to restrict the portable terminal equipment's moving in an opposite direction of the first direction.

Abstract: In an embodiment, a method and system use various sensors to determine a shape of a collection of materials (e.g., foodstuffs). A controller can determine a trajectory which achieves the desired end-state, possibly chosen from a set of feasible, collision-free trajectories to execute, and a robot executes that trajectory. The robot, executing that trajectory, scoops, grabs, or otherwise acquires the desired amount of material from the collection of materials at a desired location. The robot then deposits the collected material in the desired receptacle at a specific location and orientation.

Abstract: A substation containing a switch-gear or control-gear system, in particular a switch-gear or control-gear system includes at least one low voltage switch-gear or control-gear, with unmanned operation and maintenance. The inner room, where the switch-gear or control-gear are located in, is hermetically enclosed by an outer housing, the switch-gear or control-gear system is provided for unmanned operation and maintenance with a robotic system or manipulator, and/or the robotic system or manipulator is provided with a camera system, and/or an image recognition system and/or the inner room is locked against the outer housing by an inner, automatically operated door, and/or the robot system is implemented in such, that the robot systems acting area is extended from the inner room, partly in the area outside the inner room, but inside the outer housing, where spare parts are stored in a spare parts hand over area.

Abstract: A robot operating device includes a camera that is attached to a distal end of a robot arm or a position adjacent to the distal end and that acquires an image; a display which displays the image acquired by the camera; an operation-accepting unit which accepts an operation that is performed by an operator on the image displayed on the display unit; and a controller which moves the robot arm based on the operation accepted by the operation-accepting unit.

Abstract: Systems and methods for camera control within surgical robotic systems are provided. One system includes a computing device, multiple robot assemblies, and a surgeon console. Each robot assembly among the multiple robot assemblies includes a robotic arm. A robotic arm of a first robot assembly is coupled to an image capture device. Robotic arms of at least a subset of robot assemblies, different from the first robot assembly, are coupled to surgical instruments. The surgeon console includes multiple handles, each communicatively coupled to a robot assembly coupled to a surgical instrument. The surgeon console is configured to transmit to the computing device one or more packets that include data related to a movement of at least one handle. The computing device configured to calculate a new position of the image capture device and transmit instructions to the first robot assembly to move the image capture device to the new position.

Abstract: Embedded and/or pooled robotic process automation (RPA) robots are disclosed. A master robot initiates one or more RPA robots in a deterministic and/or probabilistic manner. For instance, when a step in an RPA workflow of the master robot is encountered where an action is not clear, some data is missing, there are multiple possible branches, etc., one or more embedded and/or pooled minion robots may be called upon by the master robot to determine the next action to take, to retrieve missing data, to determine which branch is appropriate, etc. The master robot may perform orchestration functionality with respect to the minion robot(s).

Abstract: A method for preemptive control planning can include: optionally determining a graph that defines dependencies between behaviors; executing an execution plan for an executing behavior; generating a set of predictions with the executing behavior; propagating the set of predictions to child behaviors of the executing behavior; determining child execution plans and child predictions with the child behaviors; repeating the above for descendant behaviors of each child behavior; determining a realized world state associated with completion of the execution plan; and executing a child execution plan associated with a world state prediction matching the realized world state.

Abstract: Robots, including robot arms, can interface with other modules to affect the world surrounding the robot. However, designing modules from scratch when human analogues exist is not efficient. In an embodiment, a mechanical tool, converted from human use, to be used by robots includes a monolithic adaptor having two interface components. The two interface components include a first interface component cabal be of mating with an actuated mechanism on the robot side, the second interface capable of clamping to an existing utensil. In such a way, utensils that are intended for humans can be adapted for robots and robotic arms.

Abstract: A plurality of sensors are configured to provide respective outputs that reflect a sensed value associated with engagement of a robotic arm end effector with an item. The respective outputs of the plurality of sensors are used to make a determination associated with engagement of a robotic arm end effector with an item. A first value measured by a first sensor is used to determine a first input associated with a first factor. A second value measured by a second sensor is used to determine a second input associated with a second factor. The first input and the second input are provided to a multi-factor model configured to provide, based at least in part on the first input and the second input, an output associated with engagement of the robotic arm end effector with the item. The output of the multi-factor model is used to make the determination associated with engagement of the robotic arm end effector with the item.

Abstract: Robots are deployed for handling different tasks in various field of applications. For the robots to function, task planning is required to be done. During the task planning, goal setting is done, as well as actions to be executed for corresponding to each goal are decided. Traditionally, this is carried out first and then the robots start executing the task plan, thereby failing to capture any change in the environment the robots operate, post the task plan generation. Disclosed herein is a method and system for robotic task planning in which a task plan is generated and is executed. However if the task execution fails due to change in any of the parameters/factors, then the system dynamically invokes an adaptation and re-planning mechanism which either updates the already generated task plan (by capturing the change) or generates a new task plan, which the robot can execute to achieve the goal.

Abstract: The vehicle dispatching system accepts a dispatch request from a user, selects an autonomous vehicle matching with the dispatch request from among a plurality of autonomous vehicles, and dispatches a selected autonomous vehicle to the user. The plurality of autonomous vehicles include a plurality of battery-mounted vehicles having an in-vehicle battery capable of being charged externally as an energy source. Each of the plurality of battery-mounted vehicles performs charging at a charging station when a charging level of the in-vehicle battery decreases. The vehicle dispatching system comprises a management server including a processor for executing programs stored in memory, the management server programmed to act as a charging planning unit that changes an upper limit charging level of the in-vehicle battery when charging at the charging station according to a time slot.

Abstract: Current technologies allow a robot to acquire a tool only if the tool is in a set known location, such as in a rack. In an embodiment, a method and corresponding system, can determine the previously unknown pose of a tool freely placed in an environment. The method can then calculate a trajectory that allows for a robot to move from its current position to the tool and attach with the tool. In such a way, tools can be located and used by a robot when placed at any location in an environment.

Abstract: A robot apparatus includes a storage that stores first instructional information which serves as a guide to first work operation, an acquirer that acquires second instructional information which serves as a guide to second work operation similar to the first work operation or second work operation related to the first work operation from a different apparatus having the second instructional information, and a work controller that performs the first work operation based on the first instructional information stored in the storage and the second work operation based on the second instructional information acquired by the acquirer.

Abstract: A parking facility management server for a parking facility, including a communication interface, which is designed to receive a request via a communication network from a user of the communication network to carry out at least one vehicle-specific service for a vehicle in the parking facility, and a processor, which is designed to process the request, in order to check whether or not the at least one service may be carried out in the parking facility for the vehicle in accordance with the request, the processor being further designed to ascertain a response as a function of the check. The communication interface further being designed to transmit the response via the communication network back to the user, the processor further being designed to plan and to coordinate the carrying out of the service in the event of a positive response that at least one service may be carried out.

Abstract: A machine learning system builds and uses computer models for controlling robotic performance of a task. Such computer models may be first trained using feedback on computer simulations of the robotic system performing the task, and then refined using feedback on real-world trials of the robot performing the task. Some examples of the computer models can be trained to automatically evaluate robotic task performance and provide the feedback. This feedback can be used by a machine learning system, for example an evolution strategies system or reinforcement learning system, to generate and refine the controller.