類人型機器人的世界策略:東西模式競爭分析(2025年)

目前，人形機器人的進展主要集中在優化視覺-語言-動作 (VLA) 模型、整合多模態數據以及增強其命令理解能力和解讀人類意圖的能力。訓練主要依賴世界模型、人體視訊資料和基於 VR 的遠端訓練，並且越來越重視第一人稱視角以增強感知能力。雖然最終目標是實現通用人形機器人，但由於西方和中國公司追求的技術路徑不同，其發展仍面臨重大課題。

樣品

重點

• 人形機器人專注於優化視覺-語言-動作 (VLA) 模型並增強多模態資料整合。
• 改進指令理解和人類意圖解讀是核心開發領域。
• 訓練主要依賴世界模型、人類視訊資料和基於 VR 的遠端訓練，並且越來越重視第一人稱視角。
• 最終目標是實現通用的人形機器人，但仍面臨重大技術課題。
• 西方和中國公司正在探索不同的技術路徑來實現這一目標。

目錄

第一章：機器人感知核心的視覺模型

第二章：人形機器人模型開發者的策略性舉措

第三章：TRII 的視角

Current progress in humanoid robotics is centered on optimizing vision-language-action (VLA) models, integrating multimodal data, and enhancing instruction comprehension as well as the ability to interpret human intent. Training relies heavily on world models, human video data, and VR-based remote training, with increasing emphasis on first-person perspectives to strengthen perception. While the ultimate goal is to achieve general-purpose humanoids, development remains constrained by significant challenges, leading Western and Chinese companies to pursue divergent technological pathways.

SAMPLE VIEW

Key Highlights:

• Humanoid robotics focuses on optimizing vision-language-action (VLA) models and enhancing multimodal data integration.
• Improving instruction comprehension and human intent interpretation is a core development area.
• Training relies heavily on world models, human video data, and VR-based remote training, with growing emphasis on first-person perspectives.
• The ultimate goal is to achieve general-purpose humanoids, but major technical challenges persist.
• Western and Chinese companies are pursuing different technological pathways in response.

Table of Contents

1. Vision Models as the Core of Robotic Perception

• Figure 1: Humanoid Robot Model Operation Framework
• Figure 2: Training Data for Humanoid Robots
• Table 1: Comparison of First-Person and Third-Person View Algorithms
• Figure 3: Apple HAT Model Overview
• Table 2: Summary of First-Person Datasets

2. Strategic Moves by Humanoid Robot Model Developers

• Figure 4: ViLLA Architecture

3. TRIIs View