Robots including a lifting actuator for lifting object are disclosed. In one embodiment, a robot includes a rail system extending in a system direction, a body structure coupled to the rail system, the body structure comprising an array of flexible tactile sensors, wherein each flexible tactile sensor of the array of flexible tactile sensors is operable to produce a signal determinative of a magnitude and a direction of a force applied to the flexible tactile sensor, and a lift actuator operable to move the body structure along the rail system.

A robot cleaner to avoid a stuck situation through artificial intelligence (AI) may acquire a surrounding map, based on the sensing information, determine escape path factors based on the surrounding map by using the compensation model, if the stuck situation of the robot cleaner is detected, and control the driving motor such that the robot cleaner travels, based on the determined escape path factors.

A controller is provided for interactive classification and recognition of an object in a scene using tactile feedback. The controller includes an interface configured to transmit and receive the control, sensor signals from a robot arm, gripper signals from a gripper attached to the robot arm, tactile signals from sensors attached to the gripper and at least one vision sensor, a memory module to store robot control programs, and a classifier and recognition model, and a processor to generate control signals based on the control program and a grasp pose on the object, configured to control the robot arm to grasp the object with the gripper. Further, the processor is configured to compute a tactile feature representation from the tactile sensor signals and to repeat gripping the object and computing a tactile feature representation with the set of grasp poses, after which the processor, processes the ensemble of tactile features to learn a model which is utilized to classify or recognize the object as known or unknown.

Robots, robot systems, and methods for operating the same based on environment models including haptic data are described. An environment model which includes representations of objects in an environment is accessed, and a robot system is controlled based on the environment model. The environment model incudes haptic data, which provides more effective control of the robot. The environment model is populated based on visual profiles, haptic profiles, and/or other data profiles for objects or features retrieved from respective databases. Identification of objects or features can be based on cross-referencing between visual and haptic profiles, to populate the environment model with data not directly collected by a robot which is populating the model, or data not directly collected from the actual objects or features in the environment.

Dynamically adjustable EDA measurement device may include: a dynamically formable base comprising a soft robotics material, wherein the dynamically formable base comprises a formable surface configured to be dynamically formed in response to input signals; and an EDA sensing layer affixed to the formable surface of the dynamically formable base, the EDA sensing layer comprising a plurality of electrodes arranged on a flexible substrate and configured to be connected to a power supply; wherein, in response to input signals, the formable surface of the dynamically formable base and the EDA sensing layer affixed thereto are reformed into a desired contour.

An end-effector may include a base, a plurality of underactuated fingers coupled to the base; and an adhesion gripper coupled to the base. An end-effector may include a base, an actuator, a first underactuated finger comprising a proximal link and a distal link, the proximal link including a distal end, a guide for a first tendon spaced a first distance away from the distal end of the proximal link and the distal link including a lever arm disposed on a proximal side to the distal pad and which extends in a volar direction from a first axis, and a node disposed on the lever arm sized and shaped to receive a first tendon. The end-effector may include a first revolute joint compliant in a first direction disposed between the base and the proximal link; and a second revolute joint compliant in the first direction disposed between the proximal link and the distal link.

A system and method for fabricating soft sensors that conform to arbitrary smooth geometries that include fabricating a top stretchable layer that includes a set of electrodes of soft sensors that are made of an elastic material. The system and method also include fabricating a bottom flexible layer that is composed of a thin sheet of suitable metal that is patterned using photolithography.

A visual-tactile sensing device includes a visual-tactile sensing pad useful to capture image data related to a work piece during contact with the pad and as it approaches the pad. The sensing device can be used as part of a robotic gripper or other device. One or more lights can be used to illuminate the work piece and/or project light through the pad. The pad includes a rigid base, an elastic layer structured to deform upon contact with the work piece, a light layer 70 structured to emit light at a first wavelength, and a spectrally absorbing layer structured to absorb light at a target wavelength but allow light at other wavelengths to pass. In one form the light layer can be a fluorescent layer. The spectrally absorbing layer can be a dye layer.

Disclosed herein are a biomimetic tactile sensor, a robotic skin including the same, and a preparation method therefor. The biomimetic tactile sensor includes a base layer on which a plurality of electrodes and a plurality of microphones are arranged to be distributed, a hydrogel layer disposed on the base layer, and a stimulus receiving layer disposed on the hydrogel layer, and the biomimetic tactile sensor senses a tactile stimulus accompanied by pressure, vibration, or both.

Methods, grasping systems, and computer-readable mediums storing computer executable code for grasping an object are provided. In an example, a depth image of the object may be obtained by a grasping system. A potential grasp point of the object may be determined by the grasping system based on the depth image. A tactile output corresponding to the potential grasp point may be estimated by the grasping system based on data from the depth image. The grasping system may be controlled to grasp the object at the potential grasp point based on the estimated tactile output.