Xpeng (NYSE: XPEV) has unveiled an embodied perception foundation model named GroundingPI, saying the overall benchmark score of its perception module surpassed OpenAI's frontier model GPT-6 Astra.

Adopting a perception-first technical route, the model ranked first in a comprehensive evaluation across 34 public perception benchmarks with a parameter scale of about 4 billion, and demonstrated higher data processing efficiency in robot simulation tasks, the EV maker said.

The model was developed in collaboration with research teams from universities including Peking University and the University of Hong Kong.

In a comparison on the same set of 34 perception benchmarks, weighted equally across 11 capability categories, GroundingPI achieved a score of 73.68, higher than the 71.54 scored by GPT-6 Astra, which OpenAI released in September this year.

In the project's name, Grounding corresponds to the core perception capability of visual localization, while PI is short for Physical Intelligence. The code and model weights have now been open-sourced.

Existing general-purpose large models are primarily designed for digital scenarios and face an adaptation gap when applied directly to physical scenarios such as autonomous driving and robotics, Xpeng said.

GroundingPI proposes that the primary prerequisite for embodied intelligence is accurately perceiving spatial positions.

Therefore, the model does not use robot data with joint action labels during the training phase, relying entirely on pure perception data without action labels to cover 11 capability categories including general detection, text recognition, and robot spatial localization.

Embodied intelligence is typically divided into a fast system (System 1) responsible for high-speed perception and action execution, and a slow system (System 2) responsible for task understanding and planning.

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