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

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

作者：Yichuan Yu, Youzhuo Wang, Yiming Ren, Di Feng, Yexuan Yang, Bingxi Yang, Shengxiao Gong, Yujing Sun, Yuexin Ma

来源：arXiv cs.RO（机器人）

正文

Computer Science > Robotics

arXiv:2609.26520v1 (cs)

[Submitted on 22 Sep 2026]

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

Authors:Yichuan Yu, Youzhuo Wang, Yiming Ren, Di Feng, Yexuan Yang, Bingxi Yang, Shengxiao Gong, Yujing Sun, Yuexin Ma

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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: this https URL

Subjects:

Robotics (cs.RO)

Cite as:

arXiv:2609.26520 [cs.RO]

(or

arXiv:2609.26520v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.26520

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机器人

由「前沿雷达」于 2026-09-22 采集。正文取自原文页面，已保留出处链接。标题与正文版权归原作者所有。