Pith. sign in

REVIEW 2 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2108.03332 v1 pith:YG354DBS submitted 2021-08-06 cs.RO cs.AIcs.CV

classification cs.ROcs.AIcs.CV
keywords behavioractivitiesbenchmarkactivityembodiedhumandemonstratediverse
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models

    cs.RO 2025-12 conditional novelty 6.0 of 10

    An open benchmark with 170 graded manipulation tasks shows current VLA robot policies memorize their training settings, degrade sharply under visual shifts, ignore safety constraints, and fail to compose long-horizon skills.

  2. IndoorWorld: Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment

    cs.MA 2025-06 conditional novelty 6.0 of 10

    IndoorWorld is a new multi-agent environment that combines physical task solving with social interaction, and its experiments show effects of collaboration, resource competition, and layout on agent behavior.

Pith tools