Mastering broom-like tools for object transportation animation using deep reinforcement learning

Abstract

In this paper, we propose a deep reinforcement-based approach to generate an animation of an agent using a broom-like tool to transport a target object. The tool is attached to the agent. So when the agent moves, the tool moves as well.The challenge is to control the agent to move and use the tool to push the target while avoiding obstacles. We propose a direction sensor to guide the agent's movement direction in environments with static obstacles. Furthermore, different rewards and a curriculum learning are implemented to make the agent efficiently learn skills for manipulating the tool. Experimental results show that the agent can naturally control the tool with different shapes to transport target objects. The result of ablation tests revealed the impacts of the rewards and some state components.