Tactics-Based Behavioural Planning for Goal-Driven Rigid Body Control

Abstract

Controlling rigid body dynamic simulations can pose a difficult challenge when constraints exist on the bodies' goal states and the sequence of intermediate states in the resulting animation. Manually adjusting individual rigid body control actions (forces and torques) can become a very labour-intensive and non-trivial task, especially if the domain includes a large number of bodies or if it requires complicated chains of inter-body collisions to achieve the desired goal state. Furthermore, there are some interactive applications that rely on rigid body models where no control guidance by a human animator can be offered at runtime, such as video games.

In this work, we present techniques to automatically generate intelligent control actions for rigid body simulations. We introduce sampling-based motion planning methods that allow us to model goal-driven behaviour through the use of non-deterministic Tactics that consist of intelligent, sampling-based control-blocks, called Skills. We introduce and compare two variations of a Tactics-driven planning algorithm, namely behavioural Kinodynamic Rapidly Exploring Random Trees (BK-RRT) and Behavioural Kinodynamic Balanced Growth Trees (BK-BGT). We show how our planner can be applied to automatically compute the control sequences for challenging physics-based domains and that is scalable to solve control problems involving several hundred interacting bodies, each carrying unique goal constraints.