Modeling Framework and Software Tools for Walking Robots

In research on passive dynamic walking, the aim is to study and design robots that walk naturally, i.e., with little or no control effort. McGeer [1] and others (e.g. [2, 3]) have shown that, indeed, robots can walk down a shallow slope with no actuation, only powered by gravity.\ud In this work, we derive mathematical models of walking ro- bots to better understand the dynamics that determine the walking behavior, and to design controllers that e.g. in- crease robustness against changing environments. We use the port-Hamiltonian framework, as it has the advantage of explicitly showing energy-flows inside and into the system. Thus, it allows a direct efficiency study as well as the possi- bility to connect external elements in a ‘physical’ way using ports, instead of using just torque/force signals

Projective simulation for artificial intelligence

We propose a model of a learning agent whose interaction with the environment is governed by a simulation-based projection, which allows the agent to project itself into future situations before it takes real action. Projective simulation is based on a random walk through a network of clips, which are elementary patches of episodic memory. The network of clips changes dynamically, both due to new perceptual input and due to certain compositional principles of the simulation process. During simulation, the clips are screened for specific features which trigger factual action of the agent. The scheme is different from other, computational, notions of simulation, and it provides a new element in an embodied cognitive science approach to intelligent action and learning. Our model provides a natural route for generalization to quantum-mechanical operation and connects the fields of reinforcement learning and quantum computation.Comment: 22 pages, 18 figures. Close to published version, with footnotes retaine