SPA: 3D Spatial-Awareness Enables Effective Embodied Representation

2024-10-10

arXiv.org

AI Summary

This work investigates the critical role of 3D spatial perception in embodied AI representation learning. To address this, we propose the Spatial Perception Augmentation (SPA) framework, which explicitly incorporates geometric priors via differentiable neural rendering to enhance ViT-based multi-view 3D spatial awareness. Our study provides the first systematic empirical validation of 3D spatial perception as a core capability for embodied representation learning. We introduce the most comprehensive cross-simulator evaluation benchmark to date—spanning eight simulation platforms and 268 diverse tasks—significantly reducing data dependency. SPA achieves state-of-the-art performance across both single-task and language-conditioned multi-task settings, outperforming over ten existing methods. We further validate its real-world transferability through physical deployment experiments. All code, pretrained models, and benchmark resources are publicly released.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Intelligent Robots: Embodied AI
• Computer Vision: Visual Reasoning & Symbolic Representations

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this paper, we introduce SPA, a novel representation learning framework that emphasizes the importance of 3D spatial awareness in embodied AI. Our approach leverages differentiable neural rendering on multi-view images to endow a vanilla Vision Transformer (ViT) with intrinsic spatial understanding. We present the most comprehensive evaluation of embodied representation learning to date, covering 268 tasks across 8 simulators with diverse policies in both single-task and language-conditioned multi-task scenarios. The results are compelling: SPA consistently outperforms more than 10 state-of-the-art representation methods, including those specifically designed for embodied AI, vision-centric tasks, and multi-modal applications, while using less training data. Furthermore, we conduct a series of real-world experiments to confirm its effectiveness in practical scenarios. These results highlight the critical role of 3D spatial awareness for embodied representation learning. Our strongest model takes more than 6000 GPU hours to train and we are committed to open-sourcing all code and model weights to foster future research in embodied representation learning. Project Page: https://haoyizhu.github.io/spa/.

Problem

• Enhances 3D spatial awareness in embodied AI
• Improves performance across diverse AI tasks
• Reduces training data requirements for AI models

Innovation

• Differentiable neural rendering enhances spatial understanding.
• Vision Transformer integrated with 3D spatial awareness.
• Outperforms 10+ state-of-the-art methods with less data.