VECA : A Toolkit for Building Virtual Environments to Train and Test
  Human-like Agents
  Building human-like agent, which aims to learn and think like human
intelligence, has long been an important research topic in AI. To train and
test human-like agents, we need an environment that imposes the agent to rich
multimodal perception and allows comprehensive interactions for the agent,
while also easily extensible to develop custom tasks. However, existing
approaches do not support comprehensive interaction with the environment or
lack variety in modalities. Also, most of the approaches are difficult or even
impossible to implement custom tasks. In this paper, we propose a novel
VR-based toolkit, VECA, which enables building fruitful virtual environments to
train and test human-like agents. In particular, VECA provides a humanoid agent
and an environment manager, enabling the agent to receive rich human-like
perception and perform comprehensive interactions. To motivate VECA, we also
provide 24 interactive tasks, which represent (but are not limited to) four
essential aspects in early human development: joint-level locomotion and
control, understanding contexts of objects, multimodal learning, and
multi-agent learning. To show the usefulness of VECA on training and testing
human-like learning agents, we conduct experiments on VECA and show that users
can build challenging tasks for engaging human-like algorithms, and the
features supported by VECA are critical on training human-like agents.
