发表机构
The University of Tokyo; OMRON SINIC X Corporation（东京大学; 欧姆龙SINIC X公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对接触丰富插入任务少样本学习难的问题，提出PHASE框架，利用柔顺腕部触觉信号进行相位感知分割与检索，在真实销孔插入中成功率提升13个百分点，未见位置下提升30个百分点。
AI 中文摘要
诸如销孔插入等接触丰富的装配任务，从有限的演示中学习仍然困难。虽然检索增强的模仿学习（通过相关先验数据增强目标演示）提供了一个有前景的方向，但其在接触丰富操作中的适用性在很大程度上尚未被探索。接触丰富的插入过程从搜索到插入跨越多个阶段，以原则性的方式从先验数据中检索特定阶段的经验仍是一个开放问题。我们的关键见解是，柔顺腕部使机器人能够在整个执行过程中保持接触，产生丰富的触觉和力信号，这些信号自然地揭示插入的相位结构并告知应检索什么。基于这一见解，我们提出了PHASE（相位感知分割与检索），一个用于柔顺触觉相位检索的框架，它整合了多模态接触感知表示学习、从触觉信号进行的变长相位分割，以及用于策略学习的相位一致检索。我们在五种销几何形状的真实世界销孔插入任务上评估PHASE，与在共享策略架构下从最先进方法中提取的检索策略进行比较。PHASE在整体成功率上比最强的非相位感知基线提高了13个百分点，在未见初始位置下的性能提高了30个百分点。这些结果表明，将检索与交互定义的接触相位对齐，显著提高了少样本插入学习的鲁棒性。
英文摘要
Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation learning, which augments target demonstrations with relevant prior data, offers a promising direction, its applicability to contact-rich manipulation remains largely unexplored. Contact-rich insertion unfolds over multiple phases from search to insert, and retrieving phase-specific experience from prior data in principled ways remains an open question. Our key insight is that a compliant wrist enables the robot to sustain contact throughout execution, producing rich tactile and force signals that naturally reveal the phase structure of insertion and inform what should be retrieved. Based on this insight, we present PHASE (PHase-Aware Segmentation and REtrieval), a framework for compliance-enabled tactile phase retrieval that integrates multimodal contact-aware representation learning, variable-length phase segmentation from tactile signals, and phase-consistent retrieval for policy learning. We evaluate PHASE on real-world peg-in-hole insertion across five peg geometries, comparing against retrieval strategies drawn from state-of-the-art methods under a shared policy architecture. PHASE improves the overall success rate by 13 percentage points over the strongest non-phase-aware baseline, and improves performance under unseen initial positions by 30 percentage points. These results demonstrate that aligning retrieval with interaction-defined contact phases substantially improves robustness in few-shot insertion learning.
CommentsAccepted ro IROS 2026. Project page: https://omron-sinicx.github.io/phase/
