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Building human-like agent, which aims to learn and think like humanintelligence, has long been an important research topic in AI. To train andtest human-like agents, we need an environment that imposes the agent to richmultimodal perception and allows comprehensive interactions for the agent,while also easily extensible to develop custom tasks. However, existingapproaches do not support comprehensive interaction with the environment orlack variety in modalities. Also, most of the approaches are difficult or evenimpossible to implement custom tasks. In this paper, we propose a novelVR-based toolkit, VECA, which enables building fruitful virtual environments totrain and test human-like agents. In particular, VECA provides a humanoid agentand an environment manager, enabling the agent to receive rich human-likeperception and perform comprehensive interactions. To motivate VECA, we alsoprovide 24 interactive tasks, which represent (but are not limited to) fouressential aspects in early human development: joint-level locomotion andcontrol, understanding contexts of objects, multimodal learning, andmulti-agent learning. To show the usefulness of VECA on training and testinghuman-like learning agents, we conduct experiments on VECA and show that userscan build challenging tasks for engaging human-like algorithms, and thefeatures supported by VECA are critical on training human-like agents.

VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents