A robot operable within a 3-D volume includes a gripper movable between an open position and a closed position to grasp any one of a plurality of objects, an articulatable portion coupled to the gripper and operable to move the gripper to a desired position within the 3-D volume, and an object detection system operable to capture information indicative of the shape of a first object of the plurality of objects positioned to be grasped by the gripper. A computer is coupled to the object detection system. The computer is operable to identify a plurality of possible grasp locations on the first object and to generate a numerical parameter indicative of the desirability of each grasp location, wherein the numerical parameter is at least partially defined by the next task to be performed by the robot.

A robotic gripping device is provided. The robotic gripping device includes a palm and a plurality of digits coupled to the palm. The robotic gripping device also includes a time-of-flight sensor arranged on the palm such that the time-of-flight sensor is configured to generate time-of-flight distance data in a direction between the plurality of digits. The robotic gripping device additionally includes an infrared camera, including an infrared illumination source, where the infrared camera is arranged on the palm such that the infrared camera is configured to generate grayscale image data in the direction between the plurality of digits.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a generator neural network to adapt input images.

An information processing apparatus includes an acquisition unit acquiring a first image and a second image, the first image being an image of a target area in an initial state, the second image being an image of the target area where a first object conveyed from a supply area is placed, an estimation unit estimating one or more second areas in the target area, based on a feature of a first area estimated using the first image and the second image, the first area being where the first object is placed, the one or more second areas each being an area where an object in the supply area can be placed and being different from the first area. A control unit controls a robot to convey a second object different from the first object from the supply area to any of the one or more second areas.

An estimation device includes a memory and at least one processor. The at least one processor is configured to acquire information regarding a target object. The at least one processor is configured to estimate information regarding a location and a posture of a gripper relating to where the gripper is able to grasp the target object. The estimation is based on an output of a neural model having as an input the information regarding the target object. The estimated information regarding the posture includes information capable of expressing a rotation angle around a plurality of axes.

Deep machine learning methods and apparatus related to the manipulation of an object by an end effector of a robot are described herein. Some implementations relate to training an action prediction network to predict a probability density which can include candidate actions of successful grasps by the end effector given an input image. Some implementations are directed to utilization of an action prediction network to visually servo a grasping end effector of a robot to achieve a successful grasp of an object by the grasping end effector.

A system and method for machine understanding, using program induction, includes a visual cognitive computer including a set of components designed to execute predetermined primitive functions. The method includes determining programs using a program induction engine that interfaces with the visual cognitive computer to discover programs using the predetermined primitive functions and/or executes the discovered programs based on an input.

A method of inserting an electronic components in through-hole technology, THT, into a printed circuit board, PCB by an industrial robot, based on reinforcement learning, includes grabbing, by means of a tool with universal fingers mounted to the end-effector of the industrial robot, the electronic component to be inserted into the PCB; moving the tool to a starting position being in close proximity to a final position of the electronic component; acquiring at least one image showing the tool, the electronic component and the PCB; calculating, on a basis of the at least one image, at least one movement instruction for the industrial robot; adjusting position of the tool on a basis of the at least one movement instruction, and repeating the steps until the electronic component is in the final position.

Apparatuses, method and computer program products for automatically performing sorting operations are disclosed herein. An example apparatus may comprise: an array of gripping elements, and at least one processing component configured to: obtain image data corresponding with the plurality of items; identify, from the image data, one or more characteristics of the plurality of items; determine, based at least in part on the one or more characteristics, an ordered sequence corresponding with the plurality of items; and generate a control indication to cause at least one of the gripping elements to perform the sorting operations based at least in part on the ordered sequence.

The present disclosure generally relates to a robotic gripper comprising: a body; a plurality of displacement mechanisms; a plurality of finger modules removably connected or connectable to the body, such that each finger module engages with a respective displacement mechanism; each finger module comprising a finger actuator cooperative with the other finger actuators for gripping an object; and each displacement mechanism is configured for moving the respective finger module to adjust its arrangement on the body, thereby configuring the robotic gripper for gripping the object.