Key Findings
GroundingPI has demonstrated superior performance in embodied perception benchmarks, scoring 73.68 points across 34 public evaluations. This result surpasses the 71.54 points achieved by OpenAI’s GPT-6 Astra. In simulations, systems utilizing GroundingPI achieved a higher success rate using only 50% of demonstration data, whereas comparison models required 75% of the data to reach their results.
Technical Details
The model consists of roughly 4 billion parameters and employs a “perception-first” architecture. Unlike traditional models, GroundingPI avoids robot data labeled with joint actions during training. Instead, it utilizes pure perception data across 11 specific capabilities, including:
- General detection
- Dense small-target recognition
- Referential localization
This approach enhances visual grounding, allowing the model to identify both the identity and the precise spatial location of objects within a frame. Architecturally, GroundingPI is designed as a perception-native base for the “fast system” (System 1 of embodiment), rather than serving as a replacement for the general capabilities of the “slow system” (System 2).
Background and Context
The development addresses the adaptation gap encountered when general large models, which are primarily designed for digital scenarios, are applied to physical environments. By focusing on embodied intelligence and physical AI, the research team aimed to bridge the divide between digital processing and physical world perception.
Significance and Outlook
The reduction in required demonstration data suggests a significant decrease in manual teaching costs and improved generalization across different backgrounds and materials. While the research currently remains in the prototype stage, the team has open-sourced the model weights and relevant code to facilitate further development in embodied perception.
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