A device forming a robotic hand, including a base forming a hand palm, at least two articulated structures each forming a robotic finger, each articulated structure being connected to the base by at least one articulation, at least one drive mechanism for each articulation, at least one actuator arranged to actuate the at least one drive mechanism by means of at least one flexible drive link connecting and driving the at least one drive mechanism, structure for measuring the pivoting of the at least one actuator and one or more of the articulations, a glove covering the base and the at least two articulated structures, the glove being closed so as to form, inside the glove, a volume filled with oil between the wall of the glove and the base and the at least two articulated structures. Robotic hands used in aquatic environments at great depths are also disclosed.

A plurality of sensors are configured to provide respective outputs that reflect a sensed value associated with engagement of a robotic arm end effector with an item. The respective outputs of the plurality of sensors are used to make a determination associated with engagement of a robotic arm end effector with an item. A first value measured by a first sensor is used to determine a first input associated with a first factor. A second value measured by a second sensor is used to determine a second input associated with a second factor. The first input and the second input are provided to a multi-factor model configured to provide, based at least in part on the first input and the second input, an output associated with engagement of the robotic arm end effector with the item. The output of the multi-factor model is used to make the determination associated with engagement of the robotic arm end effector with the item.

The end effector 10 includes a joint section 11 connected to a robotic arm 220, a working section 14 for performing work on an object 500, an actuator 40 is located between the joint section 11 and the working section 14 and moves the working section 14 in a first direction in which the joint section 11 and the working section 14 are aligned, a piezoelectric element 45 that drives an actuator 40.

A robot skin apparatus includes polymer membranes encapsulating a pressure sensor. The sensor includes piezo-sensitive material in contact with a pair of electrodes in spaced relationship to form a circuit. The apparatus may include a flexible substrate, with the electrodes thereon. The piezo-sensitive material may be piezoresistive film. The electrodes may be symmetrically patterned on the substrate to form a substantially circular peripheral boundary. The apparatus may include pressure sensors on opposite sides of a plane for temperature compensation, a plurality of pressure sensors arrayed on the substrate, and a data acquisition system. A method of fabricating the apparatus includes a wet lithography process for patterning the piezoresistive film. A system includes a pair of gripper fingers, an actuator connected to the fingers, a robot skin apparatus positioned on one of the fingers, and an electronic unit for receiving data from the robot skin and controlling the fingers.

Waveguides, such as light guides, made entirely of elastomeric material or with indents on an outer surface are disclosed. These improved waveguides can be used in sensors, soft robotics, or displays. For example, the waveguides can be used in a strain sensor, a curvature sensor, or a force sensor. In an instance, the waveguide can be used in a hand prosthetic. Sensors that use the disclosed waveguides and methods of manufacturing waveguides also are disclosed.

A robotic manipulator is described. The robotic manipulator includes a rigid or semi-rigid end effector that engages with objects and a sensor that detects data associated with the object. For example, the sensor can include an optical sensor or a vision-based tactile sensor that detects data associated with the object.

Dynamically adjusting, using artificial intelligence (AI), sensors and models of an autonomous roaming robotic device, which includes receiving data regarding an asset at a computer of a roaming robotic device from sensors on the robotic device. The robotic device identifies an asset at a location using the sensors, and the robotic device has instructions, received from a control system, to inspect the location or items at the location. The data is analyzed using the computer of the robotic device, and the analysis includes using historical data for the asset. An AI model is loaded using the computer of the robotic device, based on the identification of the asset. A sensor is selected using the computer of the robotic device, for conducting an inspection of the asset based on the analysis of the data and the AI model.

This disclosure relates to a technology for grasping an object while adjusting a grasping force according to stiffness of the object measured by a tactile sensor module, especially to a robot-hand, which includes a tactile sensor module for measuring a normal force applied when grasping an object, a phalange sensor module having an actuator to generate a driving force and configured to measure a rotational displacement of a motor, and a hand back control unit for operating the actuator by generating a desired displacement signal to control a grasping force so that a grasping motion is stably and accurately achieved by applying a minimum grasping force to soft object with no sliding and minimized deformation, wherein the desired displacement signal is generated based on stiffness which is calculated from the normal force data and the rotational displacement data.

A deformable sensor is provided. The deformable sensor comprises a deformable member defining an enclosure that is configured to be filled with a medium, a mechanical component disposed within the enclosure, and an optical sensor coupled to the mechanical component positioned with the enclosure. In embodiments, the mechanical component is configured to rotate at least from a first position to a second position, and the optical sensor is configured to capture first portion data associated with a first portion of the deformable member at the first position and second portion data associated with a second portion of the deformable member at the second position.

In a robot (industrial robot) system, a robot holds a workpiece by pinching the workpiece between movable claws. A controller, which controls the robot, includes a host controller that controls the robot to perform a positioning operation for positioning the hand to a grip position and a gripping operation for displacing each of the movable claws toward each other at the grip position. In the controller, a machine learning device acquires stop reference data set for gripping of the workpiece, distance data indicating a distance between each of the movable claws of the hand positioned at the grip position and the workpiece, and comparison data indicating a deformation amount of the workpiece before and after the gripping operation. The machine learning device performs machine learning using such acquired data, resulting in constructing a model used for setting an operation mode of the gripping operation.