Disclosed are various embodiments of a three-dimensional perception and object manipulation robot gripper configured for connection to and operation in conjunction with a robot arm. In some embodiments, the gripper comprises a palm, a plurality of motors or actuators operably connected to the palm, a mechanical manipulation system operably connected to the palm, a plurality of fingers operably connected to the motors or actuators and configured to manipulate one or more objects located within a workspace or target volume that can be accessed by the fingers. A depth camera system is also operably connected to the palm. One or more computing devices are operably connected to the depth camera and are configured and programmed to process images provided by the depth camera system to determine the location and orientation of the one or more objects within a workspace, and in accordance therewith, provide as outputs therefrom control signals or instructions configured to be employed by the motors or actuators to control movement and operation of the plurality of fingers so as to permit the fingers to manipulate the one or more objects located within the workspace or target volume. The gripper can also be configured to vary controllably at least one of a force, a torque, a stiffness, and a compliance applied by one or more of the plurality of fingers to the one or more objects.

The automatic operation system includes a plurality of learned imitation models and a model selecting unit. The learned imitation models are constructed by machine learning of operation history data, the operation history data being classified into several groups by an automatic classification system algorithm, the operation history data of each group being learned by the imitation model corresponding to the group. The operation history data include data indicating a surrounding environment and data indicating an operation of an operator in the surrounding environment. The model selecting unit selects one imitation model from several imitation models based on a result of classifying data indicating a given surrounding environment by the automatic classification algorithm of the classification system. The automatic operation system inputs data indicating the surrounding environment to the imitation model selected by the model selecting unit to predict an operation of the operator with respect to the surrounding environment.

A robotic surgical system for treating a patient is disclosed including a surgical tool movable relative to the patient and a user input device including a base and a space joint including a central portion movable relative to the base to effect a motion. The robotic surgical system further includes a control circuit configured to receive a user selection signal indicative of a selection between a first motion scaling profile of the motion of the surgical tool and a second motion scaling profile of the motion of the surgical tool, receive a motion control signal from the user input device indicative of a user input force, and cause the surgical tool to be moved in response to the motion control signal in accordance with the first motion scaling profile or the second motion scaling profile based on the user selection signal. The first motion scaling profile is different than the second motion scaling profile.

A machine learning method includes: a first learning step which is performed in a phase before a neural network is installed in a mobile robot and in which a stationary first obstacle is placed in a set space and the first obstacle is placed at different positions using simulation so that the neural network repeatedly learns a path from a starting point to the destination which avoids the first obstacle; and a second learning step which is performed in a phase after the neural network is installed in the mobile robot and in which, when the mobile robot recognizes a second obstacle that operates around the mobile robot in a space where the mobile robot moves, the neural network repeatedly learns a path to the destination which avoids the second obstacle every time the mobile robot recognizes the second obstacle.

A robotic kitting machine is disclosed. In various embodiments, a robotic arm is used to move an item to a location in proximity to a slot into which the item is to be inserted. Force information generated by a force sensor is received via a communication interface. The force sensor information is used to align a structure comprising the item with a corresponding cavity comprising the slot, and the item is inserted into the slot.

A system and method for programming a robot includes providing a 3D representation of workpieces to be handled by the robot, and of a working environment; synthesizing and displaying a view of the working environment comprising an image of the workpieces at respective initial positions; identifying matching features of the selected workpiece and of the working environment which are able to cooperate to hold the workpiece in a final position in the working environment, and a skill by which the matching features can be brought to cooperate; identifying an intermediate position from where applying the skill to the workpiece moves the workpiece to the final position; and adding to a motion program for the robot a routine for moving the workpiece from its initial position to the intermediate position and for applying the skill to the workpiece at the intermediate position.

A robotic automated filling and capping system is made up of a cartridge infeed conveyor, a cap infeed conveyor, an outfeed conveyor, a six-axis robot, and a selective compliance articulated robot arm, all of which are configured to work together to automatically, and sanitarily, fill and cap vape oil cartridges.

Systems and methods of assisting operator engagement with input devices include an input device configured to be operated by a hand of an operator, a repositionable structure coupled to the input device, a hand detection system, and a control unit. The control unit is configured to detect a position and an orientation of the hand using the hand detection system, determine, based on the position of the hand, a target position for the input device, wherein moving the input device from a current position of the input device to the target position moves the input device closer to a grasping position for the hand, and in response to determining that an orientation difference between the orientation of the hand and a current orientation of the input device is not greater than a threshold orientation difference, cause one or more actuators to move the input device toward the target position.

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 method for controlling a serving robot is provided. The method includes the steps of: acquiring first sensor data on at least one object placed on a support coupled to a serving robot, using at least one first sensor coupled to the serving robot; deciding whether the at least one object is a serving object on the basis of the first sensor data, and when the at least one object is decided to be a serving object, determining properties of illumination of the serving robot to be applied to the serving object on the basis of the first sensor data; and dynamically changing the properties of the illumination of the serving robot on the basis of information on surroundings acquired during travel of the serving robot.