The present disclosure discloses an autonomous mobile grabbing method for a mechanical arm based on visual-haptic fusion under a complex illumination condition, which mainly includes approaching control over a target position and feedback control over environment information.

According to the method, under the complex illumination condition, weighted fusion is conducted on visible light and depth images of a preselected region, identification and positioning of a target object are completed based on a deep neural network, and a mobile mechanical arm is driven to continuously approach the target object; in addition, the pose of the mechanical arm is adjusted according to contact force information of a sensor module, the external environment and the target object; and meanwhile, visual information and haptic information of the target object are fused, and the optimal grabbing pose and the appropriate grabbing force of the target object are selected.

By adopting the method, the object positioning precision and the grabbing accuracy are improved, the collision damage and instability of the mechanical arm are effectively prevented, and the harmful deformation of the grabbed object is reduced.

A method and apparatus for dispensing and retrieving products is provided. A system may include: a grasping head; first and second grasping members, each grasping member comprising: a top member; a post member; and first and second grasping fingers, where the first and second grasping fingers extend from the post member and are spaced apart from the top member by a predetermined distance, where the first and second grasping members are connected to the grasping head, where at least one of the first and second grasping members is movably connected to the grasping head, where the at least one of the first and second grasping members is movable relative to the other of the first and second grasping members.

According to some embodiments, a tactile information estimation apparatus may include one or more memories and one or more processors. The one or more processors are configured to input at least first visual information of an object acquired by a visual sensor to a model. The model is generated based on visual information and tactile information linked to the visual information. The one or more processors are configured to extract, based on the model, a feature amount relating to tactile information of the object.

A user can easily create a robot program. A measuring device includes a position determination processing part that determines a holding position, held by a robot hand, of a workpiece placed in a work space and determines coordinates of a fixed via point having any single attribute based on a result of measurement made by a measuring part and holding information, the fixed via point being one of an approach position of the robot hand for holding the holding position, the holding position, and a retreat position after holding, and an output part that outputs, to a robot controller, the coordinates of the fixed via point determined by the position determination processing part and attribute information showing the attribute of the fixed via point.

In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Described in detail herein is an automated fulfilment system including a computing system programmed to receive requests from disparate sources for physical objects disposed at one or more locations in a facility. The computing system can combine the requests, and group the physical objects in the requests based on object types or expected object locations. Autonomous robot devices can receive instructions from the computing system to retrieve a group of the physical objects and deposit the physical objects in storage containers.

A method and system for hand tracking in a robotic system includes a hand tracking system and a controller coupled to the hand tracking system. The controller is configured to receive, from the hand tracking system, a plurality of locations of a hand; determine if the hand is in a first hand pose based on the plurality of locations; in response to determining that the hand is in the first hand pose, and switch the robotic system to a hand trajectory detection mode. While in the hand trajectory detection mode, the control unit is configured to detect, based on hand tracking information from the hand tracking system, that the hand has performed a first hand trajectory of a plurality of known hand trajectories; and in response to detecting the first hand trajectory, change a mode of operation of the robotic system.

Compositions and methods for treating or preventing the progression of neurodegenerative diseases are provided herein. Exemplary compositions include bacterial compositions having an effective amount of viable, non-pathogenic microbes, viable, non-pathogenic bacteria, wherein at least one of the bacteria is a predatory bacteria such as Bdellovibrio bacteriovorus. The disclosed bacterial compositions can be used to treat or prevent the progression of neurodegenerative diseases such as ALS, Alzheimer's disease, Huntington's disease, and Parkinson's disease.

Teaching including adjustment of an inclination of a suction tool with respect to an object is performed in teaching of a suction position of the suction tool. A sticker affixing system includes an articulated robot, a controller, a suction tool attached to a distal end of the articulated robot, a force sensor, and a storage. The controller causes the articulated robot to execute an operation of bringing the suction tool close to a sticker on a release paper in a teaching mode, an operation of adjusting a suction position of the suction tool and an inclination in a traveling direction (roll axis X) of the suction tool in peeling off the sticker based on a signal from the force sensor, and an operation of storing the teaching data including information about the adjusted suction position and the inclination of the suction tool in the storage.

An estimation device includes a memory and at least one processor. The at least one processor is configured to acquire information regarding a target object. The at least one processor is configured to estimate information regarding a location and a posture of a gripper relating to where the gripper is able to grasp the target object. The estimation is based on an output of a neural model having as an input the information regarding the target object. The estimated information regarding the posture includes information capable of expressing a rotation angle around a plurality of axes.