Abstract: An automated system for animal waste removal is disclosed. The system comprises a wearable monitoring assembly configured to be fitted to an animal for detecting an imminent waste elimination event. This assembly includes sensors to generate postural and physiological data, which is analyzed by a processor executing a predictive model to identify the event and transmit an alert signal with the animal's location. In response to the alert, an external autonomous aerial vehicle is dispatched. The vehicle features a waste collection mechanism that is configurable in a dual-mode capacity. An interceptive mode is employed for the direct capture of waste during elimination, while a retrieval mode is used to collect waste after it has been deposited. This dual-mode functionality provides a comprehensive, timely, and automated solution for animal waste collection in various environments.

Abstract: A method for determining hard example sensor data inputs for training a task neural network is described. The task neural network is configured to receive a sensor data input and to generate a respective output for the sensor data input to perform a machine learning task. The method includes: receiving one or more sensor data inputs depicting a same scene of an environment, wherein the one or more sensor data inputs are taken during a predetermined time period; generating a plurality of predictions about a characteristic of an object of the scene; determining a level of inconsistency between the plurality of predictions; determining that the level of inconsistency exceeds a threshold level; and in response to the determining that the level of inconsistency exceeds a threshold level, determining that the one or more sensor data inputs comprise a hard example sensor data input.

Abstract: Aspects of the disclosure provide methods for controlling a first vehicle having an autonomous driving mode. In one instance, sensor data generated by one or more sensors of the first vehicle may be received. A splash and characteristics of the splash may be detected from the sensor data using a classifier. A severity of a puddle may be determined based on the characteristics of the splash and a speed of a second vehicle that caused the splash. The first vehicle may be controlled based on the severity. In another instance, a location of a puddle relative to a tire of a second vehicle is estimated using sensor data generated by one or more sensors of the first vehicle. A severity of the puddle may be determined based on the estimated location. The first vehicle may be controlled based on the severity.