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VECA

swMATH ID: 38435
Software Authors: Kwanyoung Park, Hyunseok Oh, Youngki Lee
Description: 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.
Homepage: https://arxiv.org/abs/2105.00762
Keywords: Artificial Intelligence; arXiv_cs.AI; Virtual Environments; Human-like Agents; VR-based toolkit; VECA; Human intelligence
Related Software: ViZDoom; AI2-THOR; ImageNet; Matterport3D
Referenced in: 0 Publications

Standard Articles

1 Publication describing the Software Year
VECA : A Toolkit for Building Virtual Environments to Train and Test Human-like Agents
Kwanyoung Park, Hyunseok Oh, Youngki Lee
2021