NormAct: Benchmarking Embodied Agents' Proactive Compliance with Unspoken Social Norms

Abstract

Embodied agents driven by multimodal large language models (MLLMs) can often complete everyday tasks from visual observations, but goal achievement does not establish whether they proactively respect unstated social norms. Existing benchmarks assess explicit norm judgments or constrained behavior, but rarely test whether agents infer and apply scene-relevant norms during ordinary tasks. We introduce NormAct, a benchmark of 550 TongSim scenarios in which the same goal permits norm-compliant or norm-violating action sequences. Norm-relevant evidence is embedded in each scenario while the applicable rule is omitted from the goal instruction. By progressively increasing normative guidance while holding the goal and scene fixed, NormAct tests whether compliant behavior emerges autonomously or only after prompting. Across three MLLM planners, goal achievement substantially exceeds norm compliance without guidance (67.4% versus 24.7%), while both broad and rule-specific guidance improve compliance, indicating that planners can often comply when prompted but not reliably on their own. With a fixed planner, general-norm retrieval is less effective than norm-relevant scene descriptions or generated norm cues, suggesting that identifying relevant visual evidence is a greater challenge than accessing general norm knowledge. NormAct therefore supports the development of embodied agents that pursue everyday goals while proactively respecting unstated social norms.