Robotic manipulation is essential for the widespread adoption of robots in industrial and home settings and has long been a focus within the robotics community. Advances in artificial intelligence have introduced promising learning-based methods to address this challenge, with imitation learning emerging as particularly effective. However, efficiently acquiring high-quality demonstrations remains a challenge. In this work, we introduce an immersive VR-based teleoperation setup designed to collect demonstrations from a remote human user. We also propose an imitation learning framework called Haptic Action Chunking with Transformers (Haptic-ACT). To evaluate the platform, we conducted a pick-and-place task and collected 50 demonstration episodes. Results indicate that the immersive VR platform significantly reduces demonstrator fingertip forces compared to systems without haptic feedback, enabling more delicate manipulation. Additionally, evaluations of the Haptic-ACT framework in both the MuJoCo simulator and on a real robot demonstrate its effectiveness in teaching robots more compliant manipulation compared to the original ACT. Additional materials are available at https://sites.google.com/view/hapticact.

翻译：机器人操作对于机器人在工业和家庭环境中的广泛应用至关重要，长期以来一直是机器人学领域的关注焦点。人工智能的进步为此挑战引入了有前景的基于学习的方法，其中模仿学习尤为有效。然而，高效获取高质量示范数据仍然是一个难题。在本工作中，我们引入了一种基于沉浸式VR的遥操作系统，旨在从远程人类用户处收集示范数据。我们还提出了一种名为"触觉动作分块Transformer"（Haptic-ACT）的模仿学习框架。为评估该平台，我们执行了一项拾取-放置任务并收集了50个示范片段。结果表明，与无触觉反馈的系统相比，沉浸式VR平台显著降低了操作者的指尖施力，实现了更精细的操作。此外，在MuJoCo仿真器和真实机器人上对Haptic-ACT框架的评估表明，相较于原始ACT框架，该框架能更有效地教导机器人进行柔顺操作。补充材料详见 https://sites.google.com/view/hapticact。