类人型机器人的世界策略:东西模式竞争分析(2025年)

目前，人形机器人的进展主要集中在优化视觉-语言-动作 (VLA) 模型、整合多模态数据以及增强其命令理解能力和解读人类意图的能力。训练主要依赖世界模型、人体视讯资料和基于 VR 的远端训练，并且越来越重视第一人称视角以增强感知能力。虽然最终目标是实现通用人形机器人，但由于西方和中国公司追求的技术路径不同，其发展仍面临重大课题。

样品

重点

• 人形机器人专注于优化视觉-语言-动作 (VLA) 模型并增强多模态资料整合。
• 改进指令理解和人类意图解读是核心开发领域。
• 训练主要依赖世界模型、人类视讯资料和基于 VR 的远端训练，并且越来越重视第一人称视角。
• 最终目标是实现通用的人形机器人，但仍面临重大技术课题。
• 西方和中国公司正在探索不同的技术路径来实现这一目标。

目录

第一章：机器人感知核心的视觉模型

第二章：人形机器人模型开发者的策略性举措

第三章：TRII 的视角

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