Abstract: The technology described herein is directed to a food assembly and packaging robot for automating aspects of food assembly and packaging. The food assembly and packaging robot system may include a robot system include a conveyance system, a bowl dispenser, one or more food dispensers, one or more condiment dispensers, a lidder, and a bagger, and in some instances, other components. The bowl dispenser may be configured to deposit bowls onto the conveyance system. The one or more food dispensers may be configured to deposit one or more ingredients into the bowls and the one or more condiment dispensers may be configured to deposit one or more condiments into the bowls. The lidder may be configured to position lids atop the bowls and the bagger may be configured to place the bowls into bags.

Abstract: Systems and methods are directed to an autonomy computing system for an autonomous vehicle. The autonomy computing system can include functional circuits associated with a first compute function of the autonomous vehicle. Each of the functional circuits can be configured to obtain sensor data that describes one or more aspects of an environment external to the autonomous vehicle at a current time. Each of the functional circuits can be configured to generate, over a time period and based on the sensor data, a respective output according to the specified order. The autonomy computing system can include monitoring circuits configured to evaluate an output consistency of the respective outputs, and in response to detecting an output inconsistency between two or more of the respective outputs, generate data indicative of a detected anomaly associated with the first autonomous compute function.

Abstract: Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

Abstract: Systems and methods are directed to a method for assured autonomous vehicle compute processing. The method can include providing sensor data to first and second functional circuitry of an autonomy computing system. The first and second functional circuitry can be configured to generate first and second outputs associated with a first autonomous compute function. The method can include generating, by the first and second functional circuitry in response to the sensor data, first and second output data associated with the first autonomous compute function. The method can include generating, by monitoring circuitry of the autonomy computing system, comparative data associated with differences between the first output data and the second output data. The method can include generating one or more vehicle control signals for the autonomous vehicle based at least in part on the comparative data.

Abstract: Systems and methods are directed to an autonomy computing system for an autonomous vehicle. The autonomy computing system can include functional circuits associated with a first compute function of the autonomous vehicle. Each of the functional circuits can be configured to obtain sensor data that describes one or more aspects of an environment external to the autonomous vehicle at a current time. Each of the functional circuits can be configured to generate, over a time period and based on the sensor data, a respective output according to the specified order. The autonomy computing system can include monitoring circuits configured to evaluate an output consistency of the respective outputs, and in response to detecting an output inconsistency between two or more of the respective outputs, generate data indicative of a detected anomaly associated with the first autonomous compute function.

Abstract: Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

Abstract: Systems and methods are directed to a method for assured autonomous vehicle compute processing. The method can include providing sensor data to first and second functional circuitry of an autonomy computing system. The first and second functional circuitry can be configured to generate first and second outputs associated with a first autonomous compute function. The method can include generating, by the first and second functional circuitry in response to the sensor data, first and second output data associated with the first autonomous compute function. The method can include generating, by monitoring circuitry of the autonomy computing system, comparative data associated with differences between the first output data and the second output data. The method can include generating one or more vehicle control signals for the autonomous vehicle based at least in part on the comparative data.

Abstract: Systems and methods for clustering autonomous light electric vehicles are provided. A method can include obtaining, by a computing system, data indicative of a respective location for each of a plurality of autonomous light electric vehicles; determining, by the computing system, at least a subset of the plurality of autonomous light electric vehicles to cluster within a geographic area based at least in part on the data indicative of the respective location for each of the plurality of autonomous light electric vehicles; determining, by the computing system, a point autonomous light electric vehicle and one or more follower autonomous light electric vehicles based at least in part on one or more properties of the subset of the autonomous light electric vehicles; and controlling, by the computing system, each of the follower autonomous light electric vehicles to within a threshold distance of the point autonomous light electric vehicle.

Abstract: Systems and methods for repositioning light electric vehicles are provided. A method can include obtaining, by a computing system, sensor data from one or more sensors located onboard an autonomous light electric vehicle, determining, by the computing system, one or more navigational instructions to reposition the autonomous light electric vehicle based at least in part on the sensor data, and causing, by the computing system, the autonomous light electric vehicle to initiate travel based at least in part on the one or more navigational instructions. The one or more navigational instructions can be one or more navigational instructions associated with repositioning the autonomous light electric vehicle at a light electric vehicle designated parking location, a light electric vehicle charging station, a light electric vehicle collection point, a light electric vehicle rider location, or a light electric vehicle supply positioning location.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes providing a control of the autonomous vehicle to a user associated with a vehicle service. The method includes identifying one or more release signals indicative of one or more actions of the user. The method includes releasing the control of the autonomous vehicle from the user, based at least in part on the release signals.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes providing a control of the autonomous vehicle to a user associated with a vehicle service. The method includes identifying one or more release signals indicative of one or more actions of the user. The method includes releasing the control of the autonomous vehicle from the user, based at least in part on the release signals.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes providing a control of the autonomous vehicle to a user associated with a vehicle service. The method includes identifying one or more release signals indicative of one or more actions of the user. The method includes releasing the control of the autonomous vehicle from the user, based at least in part on the release signals.

Abstract: Systems and methods for calibration and biasing are described herein. In one example, a method for determining a playing height of a user is described. The method includes receiving an identification of a first corner of a display screen from the user by associating a first position of a tracked object with the first corner using image data from a capture device, receiving an identification of a second corner of the display screen from the user by associating a second position of the tracked object with the second corner using image data from the capture device, and triangulating between the first and second corners and the tracked object to define a range of movement made by the user. The tracked object is held by the user. The method further includes defining a height at which the tracked object is held by the user based on the range of movement.

Abstract: Systems and methods for calibration and biasing are described herein. In one example, a method for determining a playing height of a user is described. The method includes receiving an identification of a first corner of a display screen from the user by associating a first position of a tracked object with the first corner using image data from a capture device, receiving an identification of a second corner of the display screen from the user by associating a second position of the tracked object with the second corner using image data from the capture device, and triangulating between the first and second corners and the tracked object to define a range of movement made by the user. The tracked object is held by the user. The method further includes defining a height at which the tracked object is held by the user based on the range of movement.