EMMA：通过第一人称人类数据扩展移动 manipulation
EMMA: Scaling Mobile Manipulation via Egocentric Human Data
AI总结：
EMMA通过第一人称人类数据和静态机器人数据训练移动 manipulation 策略，实现无需移动远程操作的端到端框架，提升现实环境中的可扩展机器人学习。
摘要：
移动 manipulation 模仿学习的扩展受到昂贵的移动机器人远程操作的限制。我们提出了Egocentric Mobile MAnipulation（EMMA），一种端到端框架，通过人类移动 manipulation 数据和静态机器人数据训练移动 manipulation 策略，从而避免移动远程操作。为此，我们共同训练人类全身运动数据和静态机器人数据。在三个现实任务的实验中，EMMA在与基于远程操作移动机器人数据训练的基线（Mobile ALOHA）相当的性能下，实现了更高的或等同的任务成功率。我们发现EMMA能够泛化到新的空间配置和场景，并且我们观察到随着人类数据小时数的增加，性能扩展呈正向趋势，为现实环境中的可扩展机器人学习开辟了新途径。该项目的细节可在https://ego-moma.github.io/上找到。
英文摘要：
Scaling mobile manipulation imitation learning is bottlenecked by expensive mobile robot teleoperation. We present Egocentric Mobile MAnipulation (EMMA), an end-to-end framework training mobile manipulation policies from human mobile manipulation data with static robot data, sidestepping mobile teleoperation. To accomplish this, we co-train human full-body motion data with static robot data. In our experiments across three real-world tasks, EMMA demonstrates comparable performance to baselines trained on teleoperated mobile robot data (Mobile ALOHA), achieving higher or equivalent task performance in full task success. We find that EMMA is able to generalize to new spatial configurations and scenes, and we observe positive performance scaling as we increase the hours of human data, opening new avenues for scalable robotic learning in real-world environments. Details of this project can be found at https://ego-moma.github.io/.
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。
