用虚拟现实与示范学习结合，实现双臂高效完成餐桌服务任务。
Taming VR Teleoperation and Learning from Demonstration for Multi-Task Bimanual Table Service Manipulation
- 通过高保真远程操控执行大部分任务，关键动作由示范学习训练
- 基于100次真人演示训练的ACT策略精准完成披萨放置任务
- 兼顾速度精度可靠性，获ICRA 2025双臂服务竞赛第一名
本技术报告介绍了在ICRA 2025年「双臂能做什么」（WBCD）竞赛中餐桌服务赛道的冠军解决方案。我们应对了一系列严苛要求的任务：展开桌布（可变形物体操作）、将披萨放入容器（抓取放置）、开闭带盖食品容器。方案融合基于虚拟现实的远程操控与示范学习（LfD），在鲁棒性与自主性间取得平衡。多数子任务通过高保真远程操控完成，披萨放置则由基于100次真人演示、初始状态随机化的ACT策略执行。通过精细整合评分规则、任务特性与现有技术能力，该方法实现了高效可靠的性能，最终夺得竞赛第一名。
原文摘要 · Abstract (English)
This technical report presents the champion solution of the Table Service Track in the ICRA 2025 What Bimanuals Can Do (WBCD) competition. We tackled a series of demanding tasks under strict requirements for speed, precision, and reliability: unfolding a tablecloth (deformable-object manipulation), placing a pizza into the container (pick-and-place), and opening and closing a food container with the lid. Our solution combines VR-based teleoperation and Learning from Demonstrations (LfD) to balance robustness and autonomy. Most subtasks were executed through high-fidelity remote teleoperation, while the pizza placement was handled by an ACT-based policy trained from 100 in-person teleoperated demonstrations with randomized initial configurations. By carefully integrating scoring rules, task characteristics, and current technical capabilities, our approach achieved both high efficiency and reliability, ultimately securing the first place in the competition.
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