Bottom-Up Synchronisation of Spontaneous Quadruped Walking Gaits for Cooperative Predator Modeling

A quadruped is an agent with four limbs which it uses for terrestrial motion. The term quadruped means "four feet". Emulating quadruped motion in animals such as horses, cheetahs, and dogs has been the focus of research since the mid-90s, but a solution to this problem is elusive. The difficulty with emulating quadruped motion is the range of leg behaviours, called gaits, that a quadruped animal can express. The list of gaits for a quadruped include, walk, trot, pace, canter, transverse gallop, rotary gallop, bound, and pronk. To obtain such a wide variety of motion, hand-built models are designed for each gait type, and the transition between gaits is structured as a state machine. This model fails when the quadruped has to perform behaviours that are not represented by the state machine. Alternatively, some work uses biologically-motivated coupled oscillators to model the motion; this still requires tuning of the coupling parameters for each of the gaits. A new idea which hasn't gained much traction yet is inspired by the self-organising properties of metronomes on a surface with feedback. Each leg is an oscillating metronome, with no interaction with the other legs except via local feedback from ground forces and the undulations of the spine. This simple model naturally transitions between the quadruped gaits as the angular velocity of the legs is increased.

My research is to explore the extend to which this model can be used for realistic simulation of quadruped behaviour. To do this, the model must be extended beyond the experimental phase by enabling it to perform actions such as turning and jumping. The model will then be used to build wolf pack simulations of wolves hunting prey via reinforcement learning. Firstly, the wolf and the prey use the self-organising quadruped model to move through the environment. This provides a solution to one of the unsolved problems in physics-based reinforcement learning which is to learn physically-realistic locomotion behaviour. Next, the brain of the wolves are represented by a neural network which is trained via reinforcement learning techniques. To make the problem interesting, a lone wolf cannot capture the prey because the prey is faster than the wolf. Wolves must therefore learn to coordinate their behaviours so that together they can capture the prey. What is interesting about this problem is that it addresses a relatively new area of learning tasks which focus on agents learning to cooperate via low-bandwidth communication; that is, a wolf does not know all the information about another wolf's decision, therefore a form of language must be learned to transmit the information between wolves. Some of the applications for this research include:
1. Streamlined design of predator-prey animations in movies, video games, or animal simulations.
2. Gaining insight into how language is used in pack animals to perform tasks beyond the capability of any single animal in the pack.
3. How morphological properties of animals (that is, the self-organising quadruped model described earlier) are essential for learning realistic animal behaviours, instead of the traditional top-down learning methods employed in some complex models which don't produce realistic motion.