In this article, we propose a framework to generate cart-pulling animation using deep learning in a multiagent environment. Mainly, two workers pull a cart using ropes and interact with crowd agents which exhibit following, wandering, and evasion behaviors. The main idea is to train a policy to learn an individual behavior of the workers and crowd agents. Furthermore, the challenge is that as the ropes are flexible, rewards are designed carefully so that the workers pull the ropes in a collaborative and consistent manner. Hence, the workers can pull the cart while avoiding collision with the crowd agents and surrounding static objects. In the stage of animation generation, we assign the policies deliberately to the workers and the crowd agents so that they interact with each other naturally. We conducted experiments on animal characters and the system could produce animations of characters with diverse behaviors.
- object transportation
- reinforcement learning