MATE: Multi-Agent Virtual Teleoperation Platform for Humanoid Collaboration Data Collection

2026-09-22

AI Summary

为解决人形机器人协作数据收集难题，研究引入MATE平台，通过虚拟远程操作实现多机器人协作，无需物理共处，并采用EAIS策略优化学习过程。

Abstract

Humanoid robots require diverse embodied experiences to acquire complex loco-manipulation and collaborative skills. However, existing humanoid data pipelines primarily focus on individual agents, while physical multi-robot collaboration remains difficult to scale due to costly hardware, dedicated spaces, and repeated resets. In this work, we introduce MATE, a Multi-Agent virtual TEleoperation platform for humanoid collaboration data collection that enables multiple geographically distributed operators to simultaneously control whole-body humanoids in a shared physics-based environment. MATE removes the need for multiple physical robots and co-located operation while preserving physically coupled interactions among humanoids, objects, and environments. Using MATE, we construct a multi-humanoid collaboration dataset comprising 24.1 hours of coordinated behavior across 2,500 joint episodes and five long-horizon tasks, including object handover, relay delivery, environment interaction, and cooperative transport. To improve learning from these interaction-rich demonstrations, we introduce EAIS, an Execution-Aligned Interaction Sampling strategy that computes sampling signals within an execution-aligned prefix and prioritizes task-progressing and interaction-critical behaviors. We evaluate MATE with representative imitation learning and vision-language-action policies across diverse collaboration tasks. Experiments demonstrate efficient data collection, effective policy learning, and zero-shot transfer from virtual demonstrations to a physical humanoid without real-world fine-tuning. Project page: https://yerik-yu.github.io/MATE/

Problem

• Humanoid Robots
• Multi-Agent Collaboration
• Data Collection
• Teleoperation
• Embodied Experiences

Innovation

• Multi-Agent Teleoperation
• Virtual Collaboration
• Execution-Aligned Interaction Sampling
• Humanoid Robots