An object of the present invention is to ascertain parts of an object that are approachable by finger portions of a hand mechanism. In an image information processing device, a housing container is retrieved from an image by using a first retrieval frame, and an object is retrieved from the image by using a second retrieval frame. A plurality of determination points are set in advance on the second retrieval frame, and a predetermined approach direction is set for each determination point. A determination is then made as to whether or not the finger portions of the hand mechanism can be caused to approach parts of one object, existing within the second retrieval frame, that correspond respectively to the determination points set on the second retrieval frame from the predetermined approach directions set in relation to the determination points.

Generating and utilizing action image(s) that represent a candidate pose (e.g., a candidate end effector pose), in determining whether to utilize the candidate pose in performance of a robotic task. The action image(s) and corresponding current image(s) can be processed, using a trained critic network, to generate a value that indicates a probability of success of the robotic task if component(s) of the robot are traversed to the particular pose. When the value satisfies one or more conditions (e.g., satisfies a threshold), the robot can be controlled to cause the component(s) to traverse to the particular pose in performing the robotic task.

A sheet conveying device and a sheet conveying method whereby it is possible to convey a sheet more accurately. This sheet conveying device includes a holder capable of holding a sheet, bringing the sheet into a stretched state while holding the sheet, and conveying the held sheet. This sheet conveying device also includes a sheet state recognizer that recognizes the state of the sheet when the sheet is held in a stretched state by the holder.

A workpiece holding apparatus include holding means for attracting and holding each workpiece in turn from among workpieces placed in a 3D space; first information acquisition means for acquiring 3D information of workpieces; candidate calculation means for calculating, based on the acquired 3D information of the workpieces, candidate holding points, the candidate holding points being, when the holding means holds each of the workpieces in turn, candidates for a holding point of that workpiece; second information acquisition means for acquiring information about each of other workpieces present within a predetermined range from each of the candidate holding points on the workpieces; and control means for selecting one of the candidate holding points based on the information about the workpiece acquired by the second information acquisition means, and controlling the holding means so that the holding means holds the workpiece at the selected candidate holding point.

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 computer system automatically selects robot end-effectors for pick-and-place applications using a model-predictive control algorithm. The system may select an end-effector to replace an existing end-effector in order to optimize (or at least increase) throughput. The system uses a predictive model of reward, where reward of each potential grasp for each end tool is parameterized by a deep neural network. The system may also use a variety of metrics to evaluate the performance of the tool-selection algorithm, and thereby improve performance of the system.

A robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function which may be a cost function for finger sliding based on a penalty for a sliding distance, a change in desired normal direction, and a wrench error associated with sliding a robot finger, and generating an object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may execute the object trajectory and the grasp sequence via the robot appendage and actuators.

A robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function based on an inverse kinematic (IK) error, a difference between an estimated required wrench and an actual wrench, and a grasp efficiency, and generating a reference object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may implement the reference object trajectory and the grasp sequence via the robot appendage and actuators.

A robotic manipulator includes one or multiple end effectors that can engage with an object, and one or multiple cameras that simultaneously observe each end effector, and the surrounding environment. For example, an end effector can include a contact surface including tactile markers which can deform when the end effector contacts the object.

A system and method for operating a transfer robot to grasp and transfer objects is disclosed. The transport robot includes a robotic gripper assembly having an array of addressable vacuum regions each configured to independently provide a vacuum to grasp a target object. The gripper assembly can be configured and/or operated according to one or more physical characteristics of targeted objects and/or corresponding scenarios.