Personal-3D: A Comprehensive Benchmark for Personalized Embodied AI Agents

Despite significant progress in Embodied AI, current agents largely operate under generic task specifications and struggle to reason about user-specific semantics that naturally arise in human-centered environments. In domestic settings, objects are often associated with particular individuals, requiring agents to interpret personalized instructions such as ownership and preference when navigating and acting in 3D spaces. We introduce PersONAL-3D (PERSonalized Object Navigation And Localization), a benchmark designed to study personalized spatial reasoning in embodied environments. PersONAL-3D focuses on domestic scenarios in which an agent must navigate to target objects associated with specific individuals, given natural-language instructions such as "find Lily's backpack". The benchmark includes more than 2,000 curated evaluation episodes across more than 30 photorealistic HM3D homes. Each episode pairs a natural-language scene description that specifies object ownership with a user-specific query, requiring models to ground personalized semantics in 3D space. PersONAL-3D supports two evaluation settings: (1) Personalized Active Navigation in previously unseen environments, and (2) Personalized Object Grounding in pre-explored scenes or directly on 3D point clouds. Experiments with state-of-the-art baselines reveal a substantial gap to human performance, with the best navigation model underperforming humans by about 45 percentage points in Success Rate and 25 percentage points in Path Efficiency, indicating that current methods struggle to perceive, act, and reason over personalized information in embodied contexts. This work highlights personalization as a critical and largely unsolved challenge for embodied AI systems operating in real-world assistive scenarios.