TAMEn：用于接触密集任务闭环数据收集的触觉感知操作引擎
TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks
AI总结：
本文提出TAMEn，通过双模采集管道和触觉-视觉学习框架，提升机器人双臂操作任务的闭环策略优化效果，实验表明任务成功率从34%提升至75%。
摘要：
手持范式为机器人操作的大规模演示收集提供了高效直观的方法。然而，通过这些方法实现接触密集的双臂操作仍是一个关键挑战，主要受制于硬件适应性和数据有效性。现有硬件设计往往针对特定夹具，并在跟踪精度与可携带性之间存在权衡。此外，演示过程中缺乏在线可行性检查导致可重播性差。更重要的是，现有手持系统在机器人执行期间难以收集交互恢复数据，缺乏必要的真实触觉信息以实现稳健的策略优化。为此，我们提出了TAMEn，一种用于接触密集任务闭环数据收集的触觉感知操作引擎。我们的系统具有跨形态可穿戴接口，可快速适应异构夹具。为平衡数据质量和环境多样性，我们实现了双模采集管道：一种利用运动捕捉的高保真演示模式，和一种利用VR跟踪的野外采集和触觉可视化恢复远程操作模式。基于此硬件，我们将大规模触觉预训练、任务特定的双臂演示和人机交互恢复数据统一到金字塔结构的数据体系中，实现闭环策略优化。实验表明，我们的可行性感知管道显著提高了演示的可重播性，而所提出的视觉-触觉学习框架在多样化的双臂操作任务中将任务成功率从34%提升至75%。我们进一步开源了硬件和数据集以促进可重复性和支持视觉-触觉操作研究。
英文摘要：
Handheld paradigms offer an efficient and intuitive way for collecting large-scale demonstration of robot manipulation. However, achieving contact-rich bimanual manipulation through these methods remains a pivotal challenge, which is substantially hindered by hardware adaptability and data efficacy. Prior hardware designs remain gripper-specific and often face a trade-off between tracking precision and portability. Furthermore, the lack of online feasibility checking during demonstration leads to poor replayability. More importantly, existing handheld setups struggle to collect interactive recovery data during robot execution, lacking the authentic tactile information necessary for robust policy refinement. To bridge these gaps, we present TAMEn, a tactile-aware manipulation engine for closed-loop data collection in contact-rich tasks. Our system features a cross-morphology wearable interface that enables rapid adaptation across heterogeneous grippers. To balance data quality and environmental diversity, we implement a dual-modal acquisition pipeline: a precision mode leveraging motion capture for high-fidelity demonstrations, and a portable mode utilizing VR-based tracking for in-the-wild acquisition and tactile-visualized recovery teleoperation. Building on this hardware, we unify large-scale tactile pretraining, task-specific bimanual demonstrations, and human-in-the-loop recovery data into a pyramid-structured data regime, enabling closed-loop policy refinement. Experiments show that our feasibility-aware pipeline significantly improves demonstration replayability, and that the proposed visuo-tactile learning framework increases task success rates from 34% to 75% across diverse bimanual manipulation tasks. We further open-source the hardware and dataset to facilitate reproducibility and support research in visuo-tactile manipulation.
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。
