Abstract: A method and system for training a machine learning (ML) model are provided. The method includes receiving a first set of parameters that relate to measuring a training status of the ML model; executing a first training operation in order to generate respective values of each of the first set of parameters; analyzing a result of the first training operation based on the respective values of each of the first plurality of parameters; and determining, based on the analysis, whether the first training operation has generated an improvement in the ML model. When the first training operation is determined as not having generated an improvement in the ML model, a second set of parameters is received, and a second training operation is executed in order to generate respective values of each of the second set of parameters.

Abstract: Methods and apparatus that provide a hardware abstraction layer (HAL) for a robot are disclosed. A HAL can reside as a software layer or as a firmware layer residing between robot control software and underlying robot hardware and/or an operating system for the hardware. The HAL provides a relatively uniform abstract for aggregates of underlying hardware such that the underlying robotic hardware is transparent to perception and control software, i.e., robot control software. This advantageously permits robot control software to be written in a robot-independent manner. Developers of robot control software are then freed from tedious lower level tasks. Portability is another advantage. For example, the HAL efficiently permits robot control software developed for one robot to be ported to another. In one example, the HAL permits the same navigation algorithm to be ported from a wheeled robot and used on a humanoid legged robot.

Abstract: Methods and apparatus that provide a hardware abstraction layer (HAL) for a robot are disclosed. A HAL can reside as a software layer or as a firmware layer residing between robot control software and underlying robot hardware and/or an operating system for the hardware. The HAL provides a relatively uniform abstract for aggregates of underlying hardware such that the underlying robotic hardware is transparent to perception and control software, i.e., robot control software. This advantageously permits robot control software to be written in a robot-independent manner. Developers of robot control software are then freed from tedious lower level tasks. Portability is another advantage. For example, the HAL efficiently permits robot control software developed for one robot to be ported to another. In one example, the HAL permits the same navigation algorithm to be ported from a wheeled robot and used on a humanoid legged robot.

Abstract: Methods and apparatus that provide a hardware abstraction layer (HAL) for a robot are disclosed. A HAL can reside as a software layer or as a firmware layer residing between robot control software and underlying robot hardware and/or an operating system for the hardware. The HAL provides a relatively uniform abstract for aggregates of underlying hardware such that the underlying robotic hardware is transparent to perception and control software, i.e., robot control software. This advantageously permits robot control software to be written in a robot-independent manner. Developers of robot control software are then freed from tedious lower level tasks. Portability is another advantage. For example, the HAL efficiently permits robot control software developed for one robot to be ported to another. In one example, the HAL permits the same navigation algorithm to be ported from a wheeled robot and used on a humanoid legged robot.