发表机构
ETH Zurich; Stanford University; mimic robotics（苏黎世联邦理工学院; 斯坦福大学; 模仿机器人公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对基于梯度的手部重定向算法易收敛到不同局部最小值影响数据质量的问题，提出基于采样的无梯度重定向方法SBR，经模拟和真实用户研究评估，其总体任务成功率最高且显著降低操作员疲劳，为灵巧操作提供高效重定向器及基准测试方法。
AI 中文摘要
基于学习的机器人操作进展，如视觉-语言-动作（VLA）模型和视频动作模型（VAM），严重依赖高质量遥操作数据。当前基于梯度的重定向算法常收敛到不同局部最小值，导致抖动影响数据质量和遥操作体验。为此引入基于采样的重定向器（SBR），一种无梯度重定向方法。通过模拟和18名参与者执行3项复杂操作任务的真实用户研究评估SBR。与基于梯度的基线相比，SBR总体任务成功率最高（54.1%），显著降低操作员认知疲劳，NASA-TLX工作量得分最低（36.4）。最终确立SBR为灵巧操作的高效、直观重定向器，并提供基准测试方法指导未来研究。
英文摘要
Advances in learning-based robotic manipulation, such as Vision-Language-Action (VLA) models and Video Action Models (VAMs), heavily rely on high-quality teleoperation data. Their capabilities are strictly upper-bounded by the quality of the underlying human demonstrations. Current gradient-based retargeting algorithms often converge to different local minima, resulting in jitter that affects data quality and teleoperation experience. To address this, we introduce the Sampling-Based Retargeter (SBR), a novel gradient-free retargeting method drawn from the rich literature of sampling-based control and explicitly designed for low-jitter, real-time kinematic retargeting. We evaluate SBR both in simulation and through a rigorous real-world user study involving 18 participants performing 3 complex manipulation tasks. Compared to gradient-based baselines, SBR achieved the highest overall task success rate (54.1%) while significantly reducing operator cognitive fatigue, recording the lowest NASA-TLX workload score (36.4 out of 100). Ultimately, we establish SBR as a highly effective, intuitive retargeter for dexterous manipulation, providing the community with a rigorous benchmarking methodology to guide future retargeting research.
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