Towards Fusing Point Cloud and Visual Representations for Imitation Learning

2025-02-17

AI Summary

To address the loss of global contextual information during multimodal fusion of point clouds and RGB images in robot manipulation imitation learning, this paper proposes FPV-Net—a framework for joint modeling of geometric structure and semantic texture. The core methodological contribution is an adaptive LayerNorm-based conditional encoding mechanism that dynamically injects global and local image tokens—extracted by a vision transformer—into a point cloud transformer encoder, thereby overcoming the contextual limitations inherent in conventional projection-based fusion approaches. Additionally, a cross-modal feature alignment strategy is introduced to enhance consistency between modalities. Evaluated on the RoboCasa benchmark, FPV-Net achieves state-of-the-art performance across all tasks, consistently outperforming both unimodal baselines and existing multimodal fusion methods.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Learning for manipulation requires using policies that have access to rich sensory information such as point clouds or RGB images. Point clouds efficiently capture geometric structures, making them essential for manipulation tasks in imitation learning. In contrast, RGB images provide rich texture and semantic information that can be crucial for certain tasks. Existing approaches for fusing both modalities assign 2D image features to point clouds. However, such approaches often lose global contextual information from the original images. In this work, we propose FPV-Net, a novel imitation learning method that effectively combines the strengths of both point cloud and RGB modalities. Our method conditions the point-cloud encoder on global and local image tokens using adaptive layer norm conditioning, leveraging the beneficial properties of both modalities. Through extensive experiments on the challenging RoboCasa benchmark, we demonstrate the limitations of relying on either modality alone and show that our method achieves state-of-the-art performance across all tasks.

Problem

• Fuse point cloud and RGB image representations.
• Enhance global and local contextual information.
• Improve imitation learning for manipulation tasks.

Innovation

• Fuses point cloud and RGB modalities
• Uses adaptive layer norm conditioning
• Achieves state-of-the-art performance