MonoDuo: 使用单机械臂学习双臂策略
MonoDuo: Using One Robot Arm to Learn Bimanual Policies
AI总结：
提出MonoDuo框架，利用单臂机器人演示和人类协作数据，通过数据增强生成合成演示，训练双臂机器人策略，在五项任务中实现零样本部署和少样本微调，成功率高达70%。
摘要：
双臂协调对于许多现实世界的操作任务至关重要，然而学习双臂机器人策略受到双臂机器人和数据集稀缺的限制。相比之下，单臂机器人在研究实验室中广泛可用。我们能否利用它们来训练双臂机器人策略？我们提出MonoDuo，一个利用单臂机器人演示与人类协作来学习双臂操作策略的框架。MonoDuo通过遥操作单臂机器人执行双臂任务的一侧，同时由人类执行另一侧来收集数据，然后交换角色以覆盖两侧。来自腕部安装和固定摄像头的RGB-D观测通过最先进的手部姿态估计、图像和点云分割以及修复，被增强为目标双臂机器人的合成演示。这些基于真实机器人运动学的合成演示用于训练双臂策略。我们在五项任务上评估MonoDuo：举箱、背包打包、叠布、拉拉链和递盘子。与仅依赖人类双臂视频的方法相比，MonoDuo能够在未见过的双臂机器人配置上实现零样本部署，成功率高达70%。仅使用25个目标机器人演示进行少样本微调，相比从头训练，成功率进一步提升65-70%，展示了MonoDuo在将单臂机器人数据高效迁移到双臂机器人策略方面的有效性。
英文摘要：
Bimanual coordination is essential for many real-world manipulation tasks, yet learning bimanual robot policies is limited by the scarcity of bimanual robots and datasets. Single-arm robots, however, are widely available in research labs. Can we leverage them to train bimanual robot policies? We present MonoDuo, a framework for learning bimanual manipulation policies using single-arm robot demonstrations paired with human collaboration. MonoDuo collects data by teleoperating a single-arm robot to perform one side of a bimanual task while a human performs the other, then swapping roles to cover both sides. RGB-D observations from a wrist-mounted and fixed camera are augmented into synthetic demonstrations for target bimanual robots using state-of-the-art hand pose estimation, image and point cloud segmentation, and inpainting. These synthetic demonstrations, grounded in real robot kinematics, are used to train bimanual policies. We evaluate MonoDuo on five tasks: box lifting, backpack packing, cloth folding, jacket zipping, and plate handover. Compared to approaches relying solely on human bimanual videos, MonoDuo enables zero-shot deployment on unseen bimanual robot configurations, achieving success rates up to 70%. With only 25 target robot demonstrations, few-shot finetuning further boosts success rates by 65-70% over training from scratch, demonstrating MonoDuo's effectiveness in efficiently transferring knowledge from single-arm robot data to bimanual robot policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
