Abstract: Training and/or utilizing a high-level neural network (NN) model, such as a sequential NN model. The high-level NN model, when trained, can be used to process a sequence of consecutive state data instances (e.g., N most recent, including a current state date instance) to generate a sequence of outputs that indicate a sequence of position deltas. The sequence of position deltas can be used to generate an intermediate target position for navigation and, optionally, an intermediate target orientation that corresponds to the intermediate target position. The intermediate target position and, optionally, the intermediate target orientation, can be provided to a low-level navigation policy, such as an MPC policy, and used by the low-level navigation policy as its goal position (and optionally goal orientation) for a plurality of iterations (e.g., until a new intermediate target position (and optionally new target orientation) is generated using the high-level NN model.

Abstract: The present application discloses systems and methods for inventorying objects. In one embodiment, a robot detects an object and sends identification data and location data associated with the detected object to a cloud computing system. The identification data may include an image of the object and/or information from a tag, code, or beacon associated with the object. In response to receiving the identification data and the location data, the cloud computing system identifies the object. The cloud computing system may also determine or create a first map associated with the identified object and a second map associated with the identified object. The first map may be associated with the current location of the object and the second map may correspond to a past location of the object. The cloud computing server may compare the first and second maps, and then send instructions to the robot based on the comparison.

Abstract: Methods and systems for comparing a 3D model of a target object to a shape-search database are provided. An example method includes using a mobile device to acquire a plurality of images of a target object, determining a 3D model based on the images, transmitting a search query that includes the 3D model, and receiving a search query result. In another example method, a server could receive a search query that includes a 3D model of an object, compare the 3D model to a shape-search database, generate a search query result based on the comparison, and transmit the search query result. The search query result could include one or more of: information regarding the target object, information regarding one or more objects similar to the target object, and a suggestion for acquiring additional images of the target object.

Abstract: Methods and systems for robot cloud computing are described. Within examples, cloud-based computing generally refers to networked computer architectures in which application execution and storage may be divided, to some extent, between client and server devices. A robot may be any device that has a computing ability and interacts with its surroundings with an actuation capability (e.g., electromechanical capabilities). A client device may be configured as a robot including various sensors and devices in the forms of modules, and different modules may be added or removed from robot depending on requirements. In some example, a robot may be configured to receive a second device, such as mobile phone, that may be configured to function as an accessory or a “brain” of the robot. A robot may interact with the cloud to perform any number of actions, such as to share information with other cloud computing devices.

Abstract: A method includes receiving first sensor data acquired by a first sensor in communication with a cloud computing system. The first sensor data has a first set of associated attributes including a time and a location at which the first sensor data was acquired. The method also includes receiving second sensor data acquired by a second sensor in communication with the cloud computing system. The second data has a second set of associated attributes including a time and a location at which the second sensor data was acquire. Further, the method includes generating a data processing result based at least in part on the first sensor data, the first set of associated attributes, the second sensor data, and the second set of associated attributes and instructing a robot in communication with the cloud computing system to perform a task based at least in part on the data processing result.

Abstract: The present application discloses systems and methods for inventorying objects. In one embodiment, a robot detects an object and sends identification data and location data associated with the detected object to a cloud computing system. The identification data may include an image of the object and/or information from a tag, code, or beacon associated with the object. In response to receiving the identification data and the location data, the cloud computing system identifies the object. The cloud computing system may also determine or create a first map associated with the identified object and a second map associated with the identified object. The first map may be associated with the current location of the object and the second map may correspond to a past location of the object. The cloud computing server may compare the first and second maps, and then send instructions to the robot based on the comparison.

Abstract: Methods and systems for robot and user interaction are provided to generate a personality for the robot. A robot may access a user device to determine or identify information about a user, and the robot may be configured to tailor a personality for interaction with the user based on the identified information. A robot may further receive data associated with the user to identify the user, such as using speech or face recognition. The robot may provide a personalized interaction or response to the user based on the determined information of the user. In some examples, a robot's personality or personalization can be transferred from one robot to another robot, or information stored on one robot can be shared with another robot over the cloud.

Abstract: Examples disclose systems and methods for recognizing objects. A method may be executable to receive a query from a robot. The query may include identification data associated with an object and contextual data associated with the object. The query may also include situational data. The method may also be executable to identify the object based at least in part on the data in the query received from the robot. Further, the method may be executable to send data associated with the identified object to the robot in response to the query.

Abstract: A method includes receiving first sensor data acquired by a first sensor in communication with a cloud computing system. The first sensor data has a first set of associated attributes including a time and a location at which the first sensor data was acquired. The method also includes receiving second sensor data acquired by a second sensor in communication with the cloud computing system. The second data has a second set of associated attributes including a time and a location at which the second sensor data was acquire. Further, the method includes generating a data processing result based at least in part on the first sensor data, the first set of associated attributes, the second sensor data, and the second set of associated attributes and instructing a robot in communication with the cloud computing system to perform a task based at least in part on the data processing result.

Abstract: Methods and systems for robot cloud computing are described. Within examples, cloud-based computing generally refers to networked computer architectures in which application execution and storage may be divided, to some extent, between client and server devices. A robot may be any device that has a computing ability and interacts with its surroundings with an actuation capability (e.g., electromechanical capabilities). A client device may be configured as a robot including various sensors and devices in the forms of modules, and different modules may be added or removed from robot depending on requirements. In some example, a robot may be configured to receive a second device, such as mobile phone, that may be configured to function as an accessory or a “brain” of the robot. A robot may interact with the cloud to perform any number of actions, such as to share information with other cloud computing devices.

Abstract: A method includes receiving first sensor data acquired by a first sensor in communication with a cloud computing system. The first sensor data has a first set of associated attributes including a time and a location at which the first sensor data was acquired. The method also includes receiving second sensor data acquired by a second sensor in communication with the cloud computing system. The second data has a second set of associated attributes including a time and a location at which the second sensor data was acquire. Further, the method includes generating a data processing result based at least in part on the first sensor data, the first set of associated attributes, the second sensor data, and the second set of associated attributes and instructing a robot in communication with the cloud computing system to perform a task based at least in part on the data processing result.

Abstract: The present application discloses shared robot knowledge bases for use with cloud computing systems. In one embodiment, the cloud computing system collects data from a robot about an object the robot has encountered in its environment, and stores the received data in the shared robot knowledge base. In another embodiment, the cloud computing system sends instructions for interacting with an object to a robot, receives feedback from the robot based on its interaction with the object, and updates data in the shared robot knowledge base based on the feedback. In yet another embodiment, the cloud computing system sends instructions to a robot for executing an application based on information stored in the shared robot knowledge base. In the disclosed embodiments, information in the shared robot knowledge bases is updated based on robot experiences so that any particular robot may benefit from prior experiences of other robots.

Abstract: Methods and systems for robotic command and operation are provided. In some examples, a robot may be configured to receive a short-form command input that is comprised of an action verb and an object/target, and to analyze contextual/situational data for event outcomes from which the robot can determine an action of a plurality of possible actions to execute. The determination and analyses functions may be performed, in whole or part, through use of a cloud computing system.

Abstract: Examples disclose systems and methods for recognizing objects. A method may be executable to receive a query from a robot. The query may include identification data associated with an object and contextual data associated with the object. The query may also include situational data. The method may also be executable to identify the object based at least in part on the data in the query received from the robot. Further, the method may be executable to send data associated with the identified object to the robot in response to the query.

Abstract: Methods and systems for robotic determination of a response to conflicting commands are provided. The robot may evaluate scenarios using variables related to the contextual/situational data for event outcomes from which the robot can determine which of two or more actions to take, as by prioritizing the actions in order of importance.