Learning Affordance Landscapes for Interaction Exploration in 3D Environments

Conference on Neural Information Processing Systems (NeurIPS)


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:

Related Publications

All Publications

NeurIPS - December 1, 2020

Continuous Surface Embeddings

Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, Andrea Vedaldi

NeurIPS - December 4, 2020

Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases

Senthil Purushwalkam, Abhinav Gupta

3DV - November 25, 2020

MonoClothCap: Towards Temporally Coherent Clothing Capture from Monocular RGB Video

Donglai Xiang, Fabian Prada, Chenglei Wu, Jessica Hodgins

CVPR - November 9, 2020

One-Shot Domain Adaptation For Face Generation

Chao Yang, Ser Nam Lim

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