English

BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments

Robotics 2021-08-10 v1 Artificial Intelligence Computer Vision and Pattern Recognition

Abstract

We introduce BEHAVIOR, a benchmark for embodied AI with 100 activities in simulation, spanning a range of everyday household chores such as cleaning, maintenance, and food preparation. These activities are designed to be realistic, diverse, and complex, aiming to reproduce the challenges that agents must face in the real world. Building such a benchmark poses three fundamental difficulties for each activity: definition (it can differ by time, place, or person), instantiation in a simulator, and evaluation. BEHAVIOR addresses these with three innovations. First, we propose an object-centric, predicate logic-based description language for expressing an activity's initial and goal conditions, enabling generation of diverse instances for any activity. Second, we identify the simulator-agnostic features required by an underlying environment to support BEHAVIOR, and demonstrate its realization in one such simulator. Third, we introduce a set of metrics to measure task progress and efficiency, absolute and relative to human demonstrators. We include 500 human demonstrations in virtual reality (VR) to serve as the human ground truth. Our experiments demonstrate that even state of the art embodied AI solutions struggle with the level of realism, diversity, and complexity imposed by the activities in our benchmark. We make BEHAVIOR publicly available at behavior.stanford.edu to facilitate and calibrate the development of new embodied AI solutions.

Keywords

Cite

@article{arxiv.2108.03332,
  title  = {BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments},
  author = {Sanjana Srivastava and Chengshu Li and Michael Lingelbach and Roberto Martín-Martín and Fei Xia and Kent Vainio and Zheng Lian and Cem Gokmen and Shyamal Buch and C. Karen Liu and Silvio Savarese and Hyowon Gweon and Jiajun Wu and Li Fei-Fei},
  journal= {arXiv preprint arXiv:2108.03332},
  year   = {2021}
}
R2 v1 2026-06-24T04:54:17.082Z