通过锚点中心适应摆脱机器人操作中的多样性陷阱
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation
AI总结：
本文提出锚点中心适应方法，通过稳定策略骨架和选择性扩展覆盖范围，解决多样性陷阱问题，提升任务可靠性和成功率。
摘要：
虽然视觉-语言-动作（VLA）模型具有广泛的能力，但部署到具体硬件需要现实适应以弥合具身差距。由于机器人演示成本高，这种适应通常必须在严格的数据预算下进行。本文识别出一个关键的多样性陷阱：标准的“最大化覆盖”启发式方法通过收集多样化的单次演示来收集数据，但这种做法可能由于非消失的估计噪声而自我毁灭。我们将这一现象形式化为覆盖-密度权衡。通过将策略误差分解为估计（密度）和外推（覆盖）项，我们表征了在固定预算下独特条件的内部最优分配。基于此分析，我们提出锚点中心适应（ACA），一种两阶段框架，首先通过在核心锚点上重复演示来稳定策略骨架，然后通过教师强制误差挖掘和受限残差更新，选择性地扩展到高风险边界。真实机器人实验验证了我们的权衡框架，并证明在相同预算下，ACA显著提高了任务的可靠性和成功率，优于标准的多样化采样策略。
英文摘要：
While Vision-Language-Action (VLA) models offer broad general capabilities, deploying them on specific hardware requires real-world adaptation to bridge the embodiment gap. Since robot demonstrations are costly, this adaptation must often occur under a strict data budget. In this work, we identify a critical diversity trap: the standard heuristic of "maximizing coverage" by collecting diverse, single-shot demonstrations can be self-defeating due to non-vanishing estimation noise. We formalize this phenomenon as a Coverage--Density Trade-off. By decomposing the policy error into estimation (density) and extrapolation (coverage) terms, we characterize an interior optimal allocation of unique conditions for a fixed budget. Guided by this analysis, we propose Anchor-Centric Adaptation (ACA), a two-stage framework that first stabilizes a policy skeleton through repeated demonstrations at core anchors, then selectively expands coverage to high-risk boundaries via teacher-forced error mining and constrained residual updates. Real-robot experiments validate our trade-off framework and demonstrate that ACA significantly improves task reliability and success rates over standard diverse sampling strategies under the same budget.
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。
