Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.

A pallet building apparatus for automatically building a pallet load of pallet load article units onto a pallet support including a frame defining a pallet building base, at least one articulated robot to transport and place the pallet load article units, a controller to control articulated robot motion and effect therewith a pallet load build, at least one three-dimensional, time of flight, camera to generate three-dimensional imaging of the pallet support and pallet load build, wherein the controller registers, from the three-dimensional camera, real time three-dimensional imaging data embodying different corresponding three-dimensional images of the pallet support and pallet load build, to determine, in real time, from the corresponding real time three-dimensional imaging data, a pallet support variance or article unit variance and generate in real time an articulated robot motion signal, the articulated robot motion signal being generated real time so as to be performed real time by the at least one articulated robot between placement of at least one pallet load article unit and a serially consecutive pallet load article unit enabling substantially continuous building of the pallet load build.

Grasping of an object, by an end effector of a robot, based on a grasp strategy that is selected using one or more machine learning models. The grasp strategy utilized for a given grasp is one of a plurality of candidate grasp strategies. Each candidate grasp strategy defines a different group of one or more values that influence performance of a grasp attempt in a manner that is unique relative to the other grasp strategies. For example, value(s) of a grasp strategy can define a grasp direction for grasping the object (e.g., “top”, “side”), a grasp type for grasping the object (e.g., “pinch”, “power”), grasp force applied in grasping the object, pre-grasp manipulations to be performed on the object, and/or post-grasp manipulations to be performed on the object.

A robotic surgical system includes an eye gaze sensing system in conjunction with a visual display of a camera image from a surgical work site. Detected gaze of a surgeon towards the display is used as input to the system. This input may be used by the system to assign an instrument to a control input device (when the user is prompted to look at the instrument), or it may be used as input to a computer vision algorithm to aid in object differentiation and seeding information, facilitating identification/differentiation of instruments, anatomical features or regions.

Provided is a robot, including: a plurality of sensors; a processor; a tangible, non-transitory, machine readable medium storing instructions that when executed by the processor effectuates operations including: capturing, with an image sensor, images of a workspace as the robot moves within the workspace; identifying, with the processor, at least one characteristic of at least one object captured in the images of the workspace; determining, with the processor, an object type of the at least one object based on characteristics of different types of objects stored in an object dictionary; and instructing, with the processor, the robot to execute at least one action based on the object type of the at least one object.

A software compensated robotic system makes use of recurrent neural networks and image processing to control operation and/or movement of an end effector. Images are used to compensate for variations in the response of the robotic system to command signals. This compensation allows for the use of components having lower reproducibility, precision and/or accuracy that would otherwise be practical.

A method for visualizing data generated by a robotic device is presented. The method includes displaying an intended path of the robotic device in an environment. The method also includes displaying a first area in the environment identified as drivable for the robotic device. The method further includes receiving an input to identify a second area in the environment as drivable and transmitting the second area to the robotic device.

An optical scanning device includes a light source unit having a light exiting portion from which a light is output, a scanning unit having a mirror supported by a supporting part and reflecting the light output from the light exiting portion while swinging the mirror around a swing axis, and a housing having an enclosed space partitioned by a plurality of wall portions including a first wall portion and a second wall portion, in which the light exiting portion and the scanning unit are placed in the enclosed space, wherein the first wall portion transmits the light reflected by the scanning unit, and the second wall portion includes a part of the light source unit.

A handling device according to an embodiment includes a manipulator, a normal grid generation unit, a hand kernel generation unit, a calculation unit, and a control unit. The normal grid generation unit converts a depth image into a point cloud, generates spatial data including an object to be grasped that is divided into a plurality of grids from the point cloud, and calculates a normal vector of the point cloud included in the grid using spherical coordinates. The hand kernel generation unit generates a hand kernel of each suction pad. The calculation unit calculates ease of grasping the object to be grasped by a plurality of suction pads based on a 3D convolution calculation using a grid including the spatial data and the hand kernel. The control unit controls a grasping operation of the manipulator based on the ease of grasping the object to be grasped by the plurality of suction pads.

A robot control method for controlling a robot including a robot arm that performs predetermined work on a work target object, the robot control method including a target-position setting step for setting, on simple shape data predicted from a plurality of projection shapes obtained by projecting the work target object from different directions, a plurality of target positions to which a control point of the robot arm in performing the predetermined work is moved and a driving step for driving the robot arm with force control based on the plurality of target positions set in the target-position setting step and force applied to the robot arm and performing the predetermined work.