BEHAVIOR: Benchmark for Everyday Household Activities in Virtual, Interactive, and Ecological Environments
Sanjana Srivastava, Chengshu Li, Michael Lingelbach, Roberto, Mart\'in-Mart\'in, Fei Xia, Kent Vainio, Zheng Lian, Cem Gokmen, Shyamal, Buch, C. Karen Liu, Silvio Savarese, Hyowon Gweon, Jiajun Wu, Li Fei-Fei

TL;DR
BEHAVIOR is a comprehensive benchmark for embodied AI involving 100 realistic household activities in simulation, designed to evaluate and advance AI agents' ability to perform complex, diverse, and ecologically valid tasks.
Contribution
It introduces a novel object-centric, predicate logic-based activity description language, a simulator-agnostic environment, and new metrics, addressing key challenges in benchmarking household activities.
Findings
State-of-the-art AI solutions struggle with BEHAVIOR's complexity.
The benchmark includes 500 human VR demonstrations as ground truth.
BEHAVIOR is publicly available for research use.
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…
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Taxonomy
TopicsReinforcement Learning in Robotics · Human Pose and Action Recognition · Human Motion and Animation
