学习3D环境中交互探索的示能性景观
Learning Affordance Landscapes for Interaction Exploration in 3D Environments
摘要：
在人类空间中运行的智能体必须能够掌握他们的环境是如何工作的：智能体可以使用什么对象，以及如何使用它们？我们引入了一种强化学习方法来探索交互，从而使一个具体的代理自主发现一个新的未映射的3D环境（如一个不熟悉的厨房）的启示景观。给定一个以自我为中心的RGB-D相机和一个高级动作空间，智能体将因最大化成功的交互而获得奖励，同时训练一个基于图像的示能表示分割模型。前者产生了一个在新环境中有效行动的策略，为下游交互任务做准备，而后者产生了一个卷积神经网络，将图像区域映射到它们允许每个动作的可能性，从而增加了探索的回报。我们用AI 2-iTHOR来证明我们的想法。结果表明，智能体可以学习如何智能地使用新的家庭环境，并为快速处理各种下游任务做好准备，例如“找到一把刀并把它放在抽屉里”。“项目页面：http://vision.cs.utexas.edu/projects/interaction-exploration/
英文摘要：
Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen). Given an egocentric RGB-D camera and a high-level action space, the agent is rewarded for maximizing successful interactions while simultaneously training an image-based affordance segmentation model. The former yields a policy for acting efficiently in new environments to prepare for downstream interaction tasks, while the latter yields a convolutional neural network that maps image regions to the likelihood they permit each action, densifying the rewards for exploration. We demonstrate our idea with AI2-iTHOR. The results show agents can learn how to use new home environments intelligently and that it prepares them to rapidly address various downstream tasks like "find a knife and put it in the drawer." Project page: http://vision.cs.utexas.edu/projects/interaction-exploration/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
