Towards planning: incremental investigations into adaptive robot control

Traditional models of planning have adopted a top-down perspective by focusing on the deliberative, conscious qualities of planning at the expense of having a system that is connected to the world through its perceptions. My thesis takes the opposing, bottom-up perspective that being firmly situated in the world is the crucial starting point to understanding planning. The central hypothesis of this thesis is that the ability to plan developed from the more primitive capacity of reactive control. Neural networks offer the most promising mechanism for investigating robot control and planning because connectionist methodology allows the task demands rather than the designer's biases to be the primary force in shaping a system's development. Input can come directly from the sensors and output can feed directly into the actuators creating a close coupling of perception and action. This interplay between sensing and acting fosters a dynamic interaction between the controller and its environment that is crucial to producing reactive behavior. Because adaptation is fundamental to the connectionist paradigm, the designer need not posit what form the internal knowledge will take or what specific function it will serve. Instead, based on the training task, the system will construct its own internal representations built directly from the sensor readings to achieve the desired control behavior. Once the system has reached an adequate level of performance at the task, its method can be dissected and a high-level understanding of its control principles can be determined. This thesis takes an incremental approach towards understanding planning. In the initial phase, several ways of representing goals are explored using a simulated robot in a one-dimensional environment. Next the model is extended to accommodate an actual physical robot and two reinforcement learning methods for adapting the network controllers are compared: a gradient descent algorithm and a genetic algorithm. Finally, the model's behavior and representations are analyzed to reveal that it contains the potential building blocks necessary for planning. By actively restricting the extent of our presuppositions about planning, we may be able to develop truly autonomous robots with radically different forms of control and planning.