Machine learning based decision making for robotic item handling

A robotic item handler comprising:

• a processing unit configured to:
• construct a machine learning model by using a combined point cloud data as an input to a convolution neural network;
• output, by the machine learning model, a decision classification indicative of a first probability associated with a first operating mode and a second probability associated with the second operating mode;
• select one of the first operating mode and the second operating mode of the robotic item handler based on the decision classification outputted by the machine learning model;
• control the robotic item handler by:
• operating a robotic arm of the robotic item handler according to the selection of the one of the first operating mode and the second operating mode;
• adjust a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight;
• and
• control the robotic item handler based on an evaluation of the selection using the pre-defined heuristic.

A non-transitory computer readable medium that stores thereon computer-executable instructions that in response to execution by a processor, perform operations comprising:

• constructing a machine learning model by using a combined point cloud data as an input to a convolution neural network;
• outputting, by the machine learning model, a decision classification indicative of a first probability associated with a first operating mode of a robotic item handler and a second probability associated with a second operating mode;
• generating a first command to operate the robotic item handler based on the decision classification;
• adjusting a first weight associated with the decision classification and a second weight associated with a pre-defined heuristic associated with past operations of the robotic item handler, based on a performance associated with the output of the machine learning model over a period of time, wherein during initial stages of learning by the machine learning model, the first weight is less than the second weight and after a substantial period of time utilized to train the machine learning model, the second weight is less than the first weight;
• and
• generating a second command to operate the robotic item handler based on an evaluation of a selection of one of the first operating mode and the second operating mode using the pre-defined heuristic.