PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception

Aviv Netanyahu*, Tianmin Shu*, Boris Katz, Andrei Barbu, and Joshua B. Tenenbaum

Massachusetts Institute of Technology

(*Equal contribution)

Introduction

Abstract

The ability to perceive and reason about social interactions in the context of physical environments is core to human social intelligence and human-machine cooperation. However, no prior dataset or benchmark has systematically evaluated physically grounded perception of complex social interactions that go beyond short actions, such as high-fiving, or simple group activities, such as gathering. In this work, we create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions by including social concepts such as helping another agent. PHASE consists of 2D animations of pairs of agents moving in a continuous space generated procedurally using a physics engine and a hierarchical planner. Agents have a limited field of view, and can interact with multiple objects, in an environment that has multiple landmarks and obstacles. Using PHASE, we design a social recognition task and a social prediction task. PHASE is validated with human experiments demonstrating that humans perceive rich interactions in the social events, and that the simulated agents behave similarly to humans. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE (SIMulation, Planning and Local Estimation), which outperforms state-of-the-art feed-forward neural networks. We hope that PHASE can serve as a difficult new challenge for developing new models that can recognize complex social interactions.

Paper and Demo

Paper

Aviv Netanyahu*, Tianmin Shu*, Boris Katz, Andrei Barbu, and Joshua B. Tenenbaum. PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception. 35th AAAI Conference on Artificial Intelligence (AAAI), 2021. [PDF] [Supp]

@inproceedings{NetanyahuPHASE2021,
  title     = {PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception},
  author    = {Aviv Netanyahu and Tianmin Shu and Boris Katz and Andrei Barbu and Joshua B. Tenenbaum},
  year      = {2021},
  booktitle = {35th AAAI Conference on Artificial Intelligence (AAAI)}
} 

PHASE Dataset

Test Set (75.8 MB)

Training set is comming soon.

Contact

Any questions? Please contact Aviv Netanyahu (avivn [at] mit.edu) and Tianmin Shu (tshu [at] mit.edu)