A plurality of sensors are configured to provide a corresponding output that reflects a sensed value associated with engagement of a robotic arm end effector with an item. The respective outputs of one or more sensors comprising the plurality of sensors are used to determine one or more inputs to a multi-modal model configured to provide, based at least in part on the one or more inputs, an output associated with slippage of the item within or from a grasp of the robotic arm end effector. A determination associated with slippage of the item within or from the grasp of the robotic arm end effector is made based at least in part on an output of the multi-modal model. A responsive action is taken based at least in part on the determination associated with slippage of the item within or from the grasp of the robotic arm end effector.

A plurality of sensors are configured to provide a corresponding output that reflects a sensed value associated with engagement of a robotic arm end effector with an item. The respective outputs of one or more sensors comprising the plurality of sensors are used to determine one or more inputs to a multi-modal model configured to provide, based at least in part on the one or more inputs, an output associated with slippage of the item within or from a grasp of the robotic arm end effector. A determination associated with slippage of the item within or from the grasp of the robotic arm end effector is made based at least in part on an output of the multi-modal model. A responsive action is taken based at least in part on the determination associated with slippage of the item within or from the grasp of the robotic arm end effector.

A technique automates robotic assembly of two parts with a fastener structure including an engaging portion and a receiving portion. A robot system includes a robot that grips a workpiece, a force sensor located on the robot to measure a force and a moment acting on the workpiece, and a controller that controls the robot. The controller monitors, while moving the workpiece in a direction along a first axis and inserting the workpiece into a part, a change in a force F in the direction along the first axis and a change in a moment M about a second axis perpendicular to the first axis measured by the force sensor to determine a state of assembly of the workpiece with the part.

A method is provided that includes controlling a robotic gripping device to cause a plurality of digits of the robotic gripping device to move towards each other in an attempt to grasp an object. The method also includes receiving, from at least one non-contact sensor on the robotic gripping device, first sensor data indicative of a region between the plurality of digits of the robotic gripping device. The method further includes receiving, from the at least one non-contact sensor on the robotic gripping device, second sensor data indicative of the region between the plurality of digits of the robotic gripping device, where the second sensor data is based on a different sensing modality than the first sensor data. The method additionally includes determining, using an object-in-hand classifier that takes as input the first sensor data and the second sensor data, a result of the attempt to grasp the object.

A control apparatus of a robot may include a state obtaining unit configured to obtain state observation data including flexible related observation data, which is observation data regarding a state of at least one of a flexible portion, a portion of the robot on a side where an object is gripped relative to the flexible portion, and the gripped object; and a controller configured to control the robot so as to output an action to be performed by the robot to perform predetermined work on the object, in response to receiving the state observation data, based on output obtained as a result of inputting the state observation data obtained by the state obtaining unit to a learning model, the learning model being learned in advance through machine learning and included in the controller.

An improvement in reliability of manufacture of an assembly including a tap is achieved. A control section of a manufacturing system for manufacturing an assembly including a tap causes a robot to perform: a step of producing a first assembly by gripping a cap having an opening and engaging the cap with a collet having a recess such that the cap is placed on the collet; a step of producing a second assembly by gripping the first assembly and inserting the first assembly into a recess of a tap holder; and a step of producing a third assembly, which is an assembly including a tap, by gripping the tap and inserting the tap through the opening of the cap of the second assembly into the recess of the collet.

A robot includes a robot hand, a robot arm, a force sensor, and a control device. The control device performs admittance control to determine a position of the robot hand in accordance with a force detected by the force sensor, and provides, to the robot arm, an instruction to move the robot hand to the determined position. Further, the control device records, as teaching data, the instruction provided to the robot arm.

Systems and methods are provided for shape controlled gripping of a workpiece. A layer jamming structure includes a membrane defining an internal cavity containing a number of overlapping material layers. A pressure system includes a pump coupled with the internal cavity. A shape conforming tool includes at least one part configured to move to apply a force to the layer jamming structure. The shape conforming tool, by operation of the part, conforms the layer jamming structure to the workpiece. The pressure system, with operation of the pump, changes a pressure in the internal cavity to impart rigidity to the layer jamming structure.

Candidate grasping models of a deformable object are applied to generate a simulation of a response of the deformable object to the grasping model. From the simulation, grasp performance metrics for stress, deformation controllability, and instability of the response to the grasping model are obtained, and the grasp performance metrics are correlated with robotic grasp features.

An autonomous tray handling robotic system is disclosed. In various embodiments, data indicating a set of output stacks to be assembled is stored, each output stack including an associated set of trays each of a corresponding tray type. Operation of one or more robots is controlled, each robot being configured to grasp, move, and place one or more trays at a time, according to a plan, to iteratively pick trays from source stacks of trays and assemble the set of output stacks, including by building each output stack by successively placing on the output stack trays picked from one or more corresponding source stacks. Each of the robots comprises a robotic arm and a tray handling end effector configured to grasp, move, and place one or more trays without assistance from another robot.