The control of artificial multipeds, whether embodied or physically simulated, is an inherently difficult task. There is considerable interest in this matter, from disciplines as diverse as robotics, computer animation, virtual reality and biology.
For computer animation, the use of motion capture has long been employed, wherein the desired human behaviour is filmed and then typically digitized to provide data for animation of an equivalent image. Whilst this provides realistic motion dynamics, particularly for the difficult simulation of bipeds, the technique suffers from the difficulty in generalizing walking motions, particularly in unpredictable conditions. The technique also imposes high demands on data storage capacity, and is inappropriate for robots.
A further approach which has therefore been developed, both for robots and for computer-generated images, relies upon a semi-physical representation of the multiped coupled with a controller to create movement patterns. Techniques such as inverse kinematics and inverse dynamics can be employed to place virtual limbs at the desired positions and accordingly to compute the required forces. The problem with this approach is that the controller tends to use finite state machines (because of the cyclical nature of ambulatory movement) which results in only partially realistic movement and requires hand turning of the parameters of the finite state machine.
In recent years, neural networks have been employed to improve the control of morpho-functional machines. So called recurrent neural networks (RNNs) have been found to be particularly appropriate for locomotion of multipeds, when used in combination with artificial evolution to optimize the parameter settings of the network. U.S. Pat. No. 5,124,918 discusses such an approach in the control of a hexapedal insect-like robot, and Ijspeert et al in “From Lampreys to Salamanders: Evolving Neural Controllers for Swimming and Walking” from Animals to Animats, Proceedings of the 5th International Conference of the Society for Adaptive Behaviour (SAB98), pages 390 to 399, MIT Press (1998) discuss the application of neural networks to artificial salamanders. Golubitsky et al in “Symmetry in locomotor central pattern generators and animal gaits”, Nature 401, pages 693-695 (1999) discuss, at a theoretical level only, the neural controllers necessary for many-legged creatures.
There are particular problems associated with the control of bipeds, both embodied as robots and as computer simulations. Specifically, the unstable dynamics of two-legged walking require that, in general, continuous active control is necessary. Additional controllers have also been necessary to control lateral and sagittal stability. For this reason, engineering techniques such as finite state machines and conventional control theory have been considered necessary. As previously explained, this brings with it the problems of mathematical tractability, the need for manual optimisation, and limits on extendability. Bipeds controlled in this manner also tend to be relatively slow moving.