Publication

Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Conference on Neural Information Processing Systems (NeurIPS)


Abstract

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/

Related Publications

All Publications

Constraining Dense Hand Surface Tracking with Elasticity

Breannan Smith, Chenglei Wu, He Wen, Patrick Peluse, Yaser Sheikh, Jessica Hodgins, Takaaki Shiratori

SIGGRAPH Asia - December 1, 2020

Synthetic Defocus and Look-Ahead Autofocus for Casual Videography

Xuaner Zhang, Kevin Matzen, Vivien Nguyen, Dillon Yao, You Zhang, Ren Ng

SIGGRAPH - July 28, 2020

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Vajda, Kurt Keutzer, Masayoshi Tomizuka

ECCV - August 24, 2020

An Exploration of Embodied Visual Exploration

Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman

arXiv - August 21, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy