用沉浸式VR+触觉反馈采集人类操作数据，让机器人学会更柔顺的抓取动作。
Haptic-ACT: Bridging Human Intuition with Compliant Robotic Manipulation via Immersive VR
- 通过虚拟现实与触觉反馈结合，远程用户可更自然地操控机器人
- 实测显示指尖受力降低40%，操作更精细，适合易碎物品处理
- 框架在仿真和真实机器人上均优于原版ACT，适合工业柔性装配场景
机器人操作对工业与家庭场景的广泛应用至关重要，一直是机器人领域的重要研究方向。人工智能的发展推动了基于学习的方法，其中模仿学习表现出显著成效。然而，高效获取高质量示范仍是难题。本文提出一种基于沉浸式VR的遥操作平台，用于远程人类用户示范数据采集，并设计了名为触觉动作分块与变压器（Haptic-ACT）的模仿学习框架。为评估平台性能，我们完成了拾取与放置任务，共收集50个示范回合。结果表明，相比无触觉反馈系统，该沉浸式平台显著降低了示范者指尖受力，支持更精细的操作。此外，在MuJoCo仿真环境及真实机器人上的测试显示，Haptic-ACT框架相较原始ACT方法能有效提升机器人的柔顺操作能力。更多材料详见：https://sites.google.com/view/hapticact。
原文摘要 · Abstract (English)
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.
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