A system comprises a database; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive at least one of sensory data from a robot and images from a camera, identify and build models of objects in an environment, wherein the model encompasses immutable properties of identified objects including mass and geometry, and wherein the geometry is assumed not to change, estimate the state including position, orientation, and velocity, of the identified objects, determine based on the state and model, potential configurations, or pre-grasp poses, for grasping the identified objects and return multiple grasping configurations per identified object, determine an object to be picked based on a quality metric, translate the pregrasp poses into behaviors that define motor forces and torques, communicate the motor forces and torques to the robot.

A weight sensor comprises a sensing system including a target piece and a sensing element, configured to provide changes of a magnetic field, being generated by motion of the target piece. The sensing element senses these changes and provides a signal representative of the position of the target piece. An integrated circuit with processing means can process signals from the sensing element. The flexible piece receives a force stimulus, so that upon exerting a force on the flexible piece by a product due to the weight of said product, the displacement of the target piece with respect to sensing elements can be sensed.

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.

The information processing device 1B mainly includes an abstract model information acquisition unit 34X, a measurement information acquisition unit 34Y, and an abstract model generation unit 34Z. The abstract model information acquisition unit 34X is configured to acquire abstract model information I5 regarding an abstract model in which dynamics in a workspace 6 where a robot 5 performs an objective task is abstracted. The measurement information acquisition unit 34Y is configured to acquire measurement information Im indicating a measurement result in the workspace 6. The abstract model generation unit 34Z is configured to generate an abstract model Σ based on the abstract model information I5 and the measurement information Im.

The present technology relates to an information processing device, an information processing method, a program, and a robot capable of estimate a value outside a detection range of a sensor. A control device of a first aspect of the present technology is a device that acquires detection results of a sensor unit composed of a plurality of sensors including a first sensor having a predetermined detection range, and a second sensor having a range in a detection range thereof in which detection by the first sensor is not possible and estimates a detected value of the first sensor outside the predetermined detection range on the basis of detection results of the second sensor. The present technology can be applied to a device that controls a robot having a hand part capable of gripping an object.

A system comprising: a database configured to store a multi-body model of a robot, the robot comprising a plurality of manipulators, and a plurality of joints and plurality of actuators and actuator motors configured to move the joints, and wherein the multi-body model includes a kinematic and geometric model of each manipulator, a catalog of models for objects to be manipulated, the models comprising a current configuration and a target configuration, and a functional mapping of sensory data to configurations of the robot and the manipulators needed to manipulate the objects; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive sensory data from within a constrained space, identify objects in the constrained space based on the received sensory data and the catalog of models, determine a target pose for the joints and the manipulators based on the sensory data and the current and target configurations associated with the identified object, and compute joint space positions to necessary to realize the target pose.

The present disclosure provides a gripper of a robot and a robot including the same. The gripper may include a case, a plurality of fingers rotatably connected to the case, and a plurality of connecting rods. A first end of each of the connecting rods may be connected to a respective one of the fingers. The gripper may also include a driving assembly connected to a second end of each of the connecting rods, and the driving assembly may be configured to drive the second end of each of the connecting rods to move along a moving direction so as to drive the plurality of finger to rotate. The gripper may further include a force detecting assembly connected to the case and the driving assembly, which may be configured to limit a position of the driving assembly along the moving direction and to detect a force from the driving assembly.

A system comprises a database; at least one hardware processor coupled with the database; and one or more software modules that, when executed by the at least one hardware processor, receive at least one of sensory data from a robot and images from a camera, identify and build models of objects in an environment, wherein the model encompasses immutable properties of identified objects including mass and geometry, and wherein the geometry is assumed not to change, estimate the state including position, orientation, and velocity, of the identified objects, determine based on the state and model, potential configurations, or pre-grasp poses, for grasping the identified objects and return multiple grasping configurations per identified object, determine an object to be picked based on a quality metric, translate the pre-grasp poses into behaviors that define motor forces and torques, communicate the motor forces and torques to the robot in order to allow the robot to perform a complex behavior generated from the behaviors.

A robotic device includes a first robotic device including a first articulated arm, a first clamping mechanism, and a blow-out portion. The first clamping mechanism includes a first clamping claw and a second clamping claw. The first clamping claw is attached to the first articulated arm, and has a first support surface. The second clamping claw has a second support surface to face the first support surface in a first axial direction orthogonal to the first support surface. The second clamping claw is movable in the first axial direction relative to the first clamping claw. The blow-out portion is provided to any one of the first clamping claw or the second clamping claw. The blow-out portion is capable of blowing out fluid in a second axial direction orthogonal to the first axial direction.

A haptic detection apparatus includes: a capacitance detection unit that detects capacitance of each of capacitors which changes according to an external force applied to a second electrode plate of a capacitance-type load sensor; a distributed load measurement unit that measures a distributed load indicating a distribution of load applied to each of the cylinders on the basis of a change amount of the capacitance of each capacitor which is detected by the capacitance detection unit; and a load information calculation unit that calculates a total load and a load center position of the external force applied to the second electrode plate of the load sensor on the basis of a relation between an expansion/contraction amount of each cylinder relative to the distributed load measured by the distributed load measurement unit and a pattern of the distributed load.