Systems and methods for training a robot to autonomously travel a route. In one embodiment, a robot can detect an initial placement in an initialization location. Beginning from the initialization location, the robot can create a map of a navigable route and surrounding environment during a user-controlled demonstration of the navigable route. After the demonstration, the robot can later detect a second placement in the initialization location, and then autonomously navigate the navigable route. The robot can then subsequently detect errors associated with the created map. Methods and systems associated with the robot are also disclosed.

Aspects of the subject disclosure may include, for example, a method in which a processing system configures one or more robots to perform tasks in an establishment, and assigns to the robots privileges and/or priorities in accordance with a policy. The method also includes detecting a situation in the establishment requiring performance of a task; facilitating the performance of the task by at least one of the robots; and dynamically reprogramming at least one of the robots in response to the situation to perform a specialized task to address the situation. Other embodiments are disclosed.

In one embodiment, a method includes receiving sensor data corresponding to an environment external of a vehicle. The sensor data include data points. The method includes determining one or more subsets of the data points. The method includes comparing the one or more subsets of the data points to one or more predetermined data patterns. Each of the one or more predetermined data patterns corresponds to an object classification. The method includes computing a confidence score for each subset of data points of the one or more subsets of the data points as corresponding to each of the one or more predetermined data patterns based on the comparison. The method includes generating a classification for an object in the environment external of the vehicle based on the confidence score.

In one embodiment, a method includes receiving autonomous-vehicle sensor data from a sensor array of an autonomous vehicle. The autonomous-vehicle sensor data indicates an object in an external environment of the autonomous vehicle. The method further includes determining a confidence score for a classification of the object. The method further includes determining an object graphic with a level of detail to visually represent the object. The level of detail of the object graphic is based on the confidence score. The method further includes providing for display a visual representation the object graphic.

When an autonomous vehicle plans a new drive mission in a confined area, a first set of environmental information, and after a time interval, a second set of environmental information, are obtained by a device flying above a confined area including the departure point and the destination point of the vehicle, each set of environmental information including locations and geometries of objects in the confined area. Then, an object that is present at the same location in the first set of environmental information and in the second set of environmental information is classified as a semi-static object, and an object that is present at different locations in the first set of environmental information and in the second set of environmental information is classified as a dynamic object. A map including location and geometry of each object classified as a semi-static object is then created.

Various implementations are provided which include receiving an instance of natural language (NL) text input indicating a task for a multi-legged robot to perform in an environment. In many implementations, the system can process the NL text input using a large language model (LLM) to generate a foot contact pattern, indicating a sequence of leg positions of the robot relative to the surface, where one or more of the legs of the robot are in contact with the surface. Additionally or alternatively, the system can generate low-level robot control output by processing the foot contact pattern using a locomotion controller.

A mobile internet of things (IoT) unit for cleaning grease vents, herein referred to as the unit, is disclosed. The unit is comprised of the following parts: a mobile platform with magnetic tracks; a mobile device software application (app); cleaning attachments such as power washers and lasers, sensors such as conductivity meters (to measure buildup), air temperature, velocity and pressure; recording devices such as digital still and streaming cameras; a microcontroller with wireless communications; onboard lighting and a rechargeable battery. Additional details regarding the unit are examined further in this disclosure.

A safety system assembly (1) serves to monitor a zone (5), such as a warehouse or a factory, in which objects (8), such as autonomously driving vehicles (4) and persons (8a), move together. The safety system assembly (1) comprises a central processing device (3) that is configured to receive sensor data (6) from a plurality of monitoring units (2), in which sensor data (6) the objects (8) detected in the monitored zone (5) by the monitoring units (2) are included. The central processing device (3) is configured to consolidate the received sensor data (6). The central processing device (3) is configured to create object lists (7) from the consolidated sensor data, with the object lists (7) including the detected objects (8) together with the respective object information, and to transmit these object lists (7) to the autonomously driving vehicles (4).

Herein, a perception statistical performance model (PSPM) for modeling a perception slice of a runtime stack for an autonomous vehicle or other robotic system may be used e.g. for safety/performance testing. A PSPM is configured to: receive a computed perception ground truth t; determine from the perception ground truth t, based on a set of learned parameters, a probabilistic perception uncertainty distribution of the form p(e|t), p(e|t,c), in which p(e|t,c) denotes the probability of the perception slice computing a particular perception output e given the computed perception ground truth t and the one or more confounders c, and the probabilistic perception uncertainty distribution is defined over a range of possible perception outputs, the parameters learned from a set of actual perception outputs generated using the perception slice to be modeled, wherein each confounder is a variable of the PSPM whose value characterized a physical condition on which p(e|t,c) depends.

A navigation system for navigating an autonomous mobile robot in an environment is provided. The navigation system includes at least one optical sensor attached to the autonomous mobile robot, a controller in communication with the at least one optical sensor, and a plurality of optical identifiers distributed within the environment at fixed locations and detectable by the at least one optical sensor. Each of the plurality of optical identifiers encodes a location within the environment. The controller is configured to obtain pictures of the environment via the at least one optical sensor, detect visible optical identifiers of the plurality of optical identifiers, which are within a field of view of the at least one optical sensor, decode the visible optical identifiers, and navigate the autonomous mobile robot based on real-time localizations of the autonomous mobile robot within the environment using the decoded visible optical identifiers.