Autonomous motion learning for near optimal control

Human intelligence has appealed to the robotics community for a long time; specifically, a person\u27s ability to learn new tasks efficiently and eventually master the task. This ability is the result of decades of development as a person matures from an infant to an adult and a similar developmental period seems to be required if robots are to obtain the ability to learn and master new skills. Applying developmental stages to robotics is a field of study that has been growing in acceptance. The paradigm shift is from directly pursuing the desired task to progressively building competencies until the desired task is reached. This dissertation seeks to apply a developmental approach to autonomous optimization of robotic motions, and the methods presented extend to function shaping and parameter optimization. Humans have a limited ability to concentrate on multiple tasks at once. For robots with many degrees of freedom, human operators need a high-level interface, rather than controlling the positions of each angle. Motion primitives are scalable control signals that have repeatable, high-level results. Examples include walking, jumping or throwing where the result can be scaled in terms of speed, height or distance. Traditionally, motion primitives require extensive, robot-specific analysis making development of large databases of primitives infeasible. This dissertation presents methods of autonomously creating and refining optimal inverse functions for use as motion primitives. By clustering contiguous local optima, a continuous inverse function can be created by interpolating results. The additional clusters serve as alternatives if the chosen cluster is poorly suited to the situation. For multimodal problems, a population based optimization can efficiently search a large space. Staged learning offers a path to mimic the progression from novice to master, as seen in human learning. The dimension of the input wave parameterization, which is the number degrees of freedom for optimization, is incremented to allow for additional improvement. As the parameterization increases in order, the true optimal continuous-time control signal is approached. All previous experiences can be directly moved to the higher parameterization when expanding the parameterization, if a proper parameterization is selected. Incrementally increasing complexity and retaining experience efficiently optimizes to high dimensions when contrasted with undirected global optimizations, which would need to search the entire high dimension space. The method presented allows for unbounded resolution since the parameterization is not fixed at programming. This dissertation presents several methods that make steps towards the goal of learning and mastering motion-related tasks without programmed, task-specific heuristics. Trajectory optimization based on a high-level system description has been demonstrated for a robotic arm performing a pick-place task. In addition, the inverse optimal function was applied to optimizing robotic tracking precision in a method suitable for online tracking. Staging of the learning is able determine an optimal motor spin-up waveform despite large variations in system parameters. Global optimization, using a population based search, and unbounded resolution increasing provide the foundation for autonomously developing scalable motions superior to what can be designed by hand