| Equipped with the above-mentioned reasoning capabilities, the robot can infer parametrizations of under-specified actions, e.g., if the robot is required to bring a plate from the counter to the dining table, it can infer where is the counter positioned, where should it stand in order to be able to see objects on the counter, when it finds a plate on the counter where should it stand in order to grasp it, where should it stand to be able to put down the object in a location on the dining table etc. This information can then be used at run-time when actually performing the action on the real robot, or used in a simulation in order to find the best parametrization based on a number of trials. Which parametrization is the "best" is then decided based on a certain cost function, which can, for example, be a distance the robot would be driving throughout the action execution. | Equipped with the above-mentioned reasoning capabilities, the robot can infer parametrizations of under-specified actions, e.g., if the robot is required to bring a plate from the counter to the dining table, it can infer where is the counter positioned, where should it stand in order to be able to see objects on the counter, when it finds a plate on the counter where should it stand in order to grasp it, where should it stand to be able to put down the object in a location on the dining table etc. This information can then be used at run-time when actually performing the action on the real robot, or used in a simulation in order to find the best parametrization based on a number of trials. Which parametrization is the "best" is then decided based on a certain cost function, which can, for example, be a distance the robot would be driving throughout the action execution. |
