Machine-learned robot fleet management for value chain networks

A method of assigning robotic fleet resources to tasks, comprising:

• storing, in a computer-readable storage system, a fleet resource inventory that indicates a plurality of fleet resources that can be assigned to perform at least one task in a set of tasks, and for each respective fleet resource of the plurality of fleet resources, a set of features of the respective fleet resource and a status of the respective fleet resource;
• storing, in the computer-readable storage system, a set of task definitions that is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes;
• receiving a request for a robotic fleet to perform a job, wherein:
• the robotic fleet includes a set of robot operating units,
• at least one robot of the set of robot operating units includes a control interface module and a physical interface module, and
• the control interface module controls the physical interface module;
• determining a job definition data structure based on the request, wherein the job definition data structure indicates a set of tasks that are to be performed in performance of the job;
• determining a robotic fleet configuration data structure corresponding to the job based on the set of tasks and a fleet resource inventory, and for each respective fleet resource of the plurality of fleet resources, the fleet resource inventory indicates a set of features of the respective fleet resource, a set of configuration requirements of the respective fleet resource, and a status of the respective fleet resource, wherein the robotic fleet configuration data structure assigns a plurality of resources selected from the fleet resource inventory to the set of tasks defined in the job definition data structure based on the set of task definitions, the respective set of features of each fleet resource, and the respective status of each fleet resource;
• deploying the robotic fleet to perform the job, wherein performing the job includes performing at least one task by at least one resource of the plurality of resources assigned to perform the at least one task;
• applying an outcome of performing the at least one task by at least one resource of the plurality of resources assigned to perform the at least one task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions;
• and
• updating the set of task definitions based on a result of applying the outcome to the machine learning system, wherein:
• the set of task definitions indicates a type of end effector of at least one robot operating unit of the set of robot operating units,
• updating the set of task definitions includes communicating with the control interface module to update the type of end effector of the at least one robot operating unit of the set of robot operating units, and
• updating the type of end effector includes controlling the physical interface module, by the control interface module, to update the type of end effector of the at least one robot operating unit of the set of robot operating units.