Systems and methods of the present disclosure provide a control solution for a robotic actuator. The actuator can have one or two degrees of freedom of control, and can connect with a platform using an arm. The arm can have at least two degrees of freedom of control, and the platform can have at least two degrees of freedom of control. The platform can be subjected to unpredictable forces requiring a control response. The control solution can be generated using operational space control, using the degrees of freedom of the arm, platform, and actuator.

Techniques described herein relate to using reduced-dimensionality embeddings generated from robot sensor data to identify predetermined semantic labels that guide robot interaction with objects. In various implementations, obtaining, from one or more sensors of a robot, sensor data that includes data indicative of an object observed in an environment in which the robot operates. The sensor data may be processed utilizing a first trained machine learning model to generate a first embedded feature vector that maps the data indicative of the object to an embedding space. Nearest neighbor(s) of the first embedded feature vector may be identified in the embedding space. Semantic label(s) may be identified based on the nearest neighbor(s). A given grasp option may be selected from enumerated grasp options previously associated with the semantic label(s). The robot may be operated to interact with the object based on the pose and using the given grasp option.

Method and apparatus for training a machine learning model for controlling a robotic picking device having a suction device end effector. A robotic control operation and a candidate contact point for holding a first item using a suction device of a robotic picking arm are determined, by processing information describing the first item as an input to a machine learning model. A seal quality metric is estimated for the candidate contact point, based on a predicted deformed n-dimensional shape of the suction device and a n-dimensional shape associated with the first item. One or more weights within the machine learning model are refined based on the estimated seal quality metric.

An object manipulation apparatus according to an embodiment of the present disclosure includes a memory and a hardware processor coupled to the memory. The hardware processor is configured to: calculate, based on an image in which one or more objects to be grasped are contained, an evaluation value of a first behavior manner of grasping the one or more objects; generate information representing a second behavior manner based on the image and a plurality of evaluation values of the first behavior manner; and control actuation of grasping the object to be grasped in accordance with the information being generated.

A machine learning device that learns a waiting time for at least one of grasping of a workpiece by a spindle chuck and releasing of the workpiece by a loader chuck during transfer of the workpiece between the spindle chuckthat grasps and sends the workpiece and the loader chuck that grasps and receives the workpiece. The machine learning device includes a state observing unit that observes the waiting time and a FB current from a drive unit that moves the loader chuck as state variables, and a learning unit that learns the waiting time with which a transfer time of the workpiece is shortened, in accordance with a data set created based on the state variables.

A learning device includes storage and a learning section. The storage stores therein a learning model. The learning model causes the learning model to learn training data including captured image data and gripping force data. The captured image data corresponds to data to be input to the learning model. The gripping force data corresponds to data to be output from the learning model. The captured image data is data generated by capturing an image of a work to be gripped by a robotic device. The gripping force data is data indicating a gripping force of the robotic device when gripping the work.

A robotic gripping device, system and method are disclosed. The robotic device includes an end effector having at least one finger, the fingers being configured for manipulating objects in the vicinity of the device under computer control. The device is configured for manipulating objects of varying sizes, dimensions and positions with reference to the device, without requiring information as to the precise location of the object with reference to the device.

A system and method of operation of a robotic system including: receiving a sensor reading associated with a target object; generating a base plan for performing a task on the target object, wherein generating the base plan includes determining a grip point and one or more grip patterns associated with the grip point for gripping the target object based on a location of the grip point relative to a designated area, a task location, and another target object; implementing the base plan for performing the task by operating an actuation unit and one or more suction grippers according to a grip pattern rank, to generate an established grip on the target object, wherein the established grip is at a grip pattern location associated with the grip patterns; measuring the established grip; comparing the established grip to a force threshold; and re-gripping the target object based on the established grip falling below the force threshold.

A workpiece transport robot configured to determine whether a workpiece gripping failure has occurred, the workpiece transport robot including a transport robot main body having a driving mechanism configured to move a held workpiece; a robot hand having a first chuck and a second chuck configured to grip workpieces on both front and back faces of the robot hand; a robot hand rotating mechanism configured to axially support the robot hand and position the robot hand in a rotational direction with a servomotor, the robot hand supported with the transport robot main body via a rotation shaft to which first chuck and second chuck are symmetrically positioned, and a control device configured to compare measurement state information of the robot hand, of which information being based on torque information obtained by measuring and driving the servomotor; with workpiece gripping information obtained from a work program of the robot hand.

According to one embodiment, a handling device includes: a holding part that includes two or more supporting parts and is capable of holding an object by gripping the object with the two or more supporting parts; a calculation part configured to calculate a safety factor indicating safety of a state of the holding part holding the object; and a controller configured to cause the holding part to hold the object according to the safety factor.