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In this paper we address the problem of visual reaction: the task ofinteracting with dynamic environments where the changes in the environment arenot necessarily caused by the agent itself. Visual reaction entails predictingthe future changes in a visual environment and planning accordingly. We studythe problem of visual reaction in the context of playing catch with a drone invisually rich synthetic environments. This is a challenging problem since theagent is required to learn (1) how objects with different physical propertiesand shapes move, (2) what sequence of actions should be taken according to theprediction, (3) how to adjust the actions based on the visual feedback from thedynamic environment (e.g., when objects bouncing off a wall), and (4) how toreason and act with an unexpected state change in a timely manner. We propose anew dataset for this task, which includes 30K throws of 20 types of objects indifferent directions with different forces. Our results show that our modelthat integrates a forecaster with a planner outperforms a set of strongbaselines that are based on tracking as well as pure model-based and model-freeRL baselines. The code and dataset are available atgithub.com/KuoHaoZeng/Visual_Reaction.

Visual Reaction: Learning to Play Catch with Your Drone