A robotic assembly operation is provided for assembling a second part to a first part. During setup of the assembly operation, control parameters and a control scheme are set and changed by simulating the operation and testing whether performance requirements are met. A dry run may be performed thereafter, and test data may be collected after running the simulation to determine if the performance requirements are satisfied during the dry run. During production, production data may also be collected and control parameters may be tuned when changes occur during production in order to maintain stable assembly.

Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure. The embodiments of the proposed disclosure provide for generating one or more control system solutions for a plurality of robotics environment by acquiring a robotics domain knowledge corresponding to the plurality of robotics environments; extracting one or more solution specifications based upon the robotics domain knowledge; translating the one or more solution specifications into one or more design solutions; generating, the one or more control system solutions for the plurality of robotics environments; and optimizing the one or more control system solutions generated by performing, based upon a set of task execution logs executed, a close loop verification to validate a plurality of commands and a plurality of state transitions executing in the plurality of robotics environments.

Systems and methods for generating control system solutions for robotics environments is provided. The traditional systems and methods provide robotics solutions but specialized to only a particular robotic application, domain, and selected structure. The embodiments of the proposed disclosure provide for generating one or more control system solutions for a plurality of robotics environment by acquiring a robotics domain knowledge corresponding to the plurality of robotics environments; extracting one or more solution specifications based upon the robotics domain knowledge; translating the one or more solution specifications into one or more design solutions; generating, the one or more control system solutions for the plurality of robotics environments; and optimizing the one or more control system solutions generated by performing, based upon a set of task execution logs executed, a close loop verification to validate a plurality of commands and a plurality of state transitions executing in the plurality of robotics environments.

A robot design system, and associated method, that is particularly well-suited for legged robots (e.g., monopods, bipeds, and quadrupeds). The system implements three stages or modules: (a) a motion optimization module; (b) a morphology optimization module; and (c) a link length optimization module. The motion optimization module outputs motion trajectories of the robot's center of mass (COM) and force effectors. The morphology optimization module uses as input the optimized motion trajectories and a library of modular robot components and outputs an optimized robot morphology, e.g., a parameterized mechanical design in which the number of links in each of the legs and other parameters are optimized. The link length optimization module takes this as input and outputs optimal link lengths for a particular task such that the design of a robot is more efficient. The system solves the problem of automatically designing legged robots for given locomotion tasks by numerical optimization.