发表机构
Faculty of Mechanical Engineering, Sungkyunkwan University; Faculty of Intelligent Robotics, Sungkyunkwan University（成均馆大学机械工程学院; 成均馆大学智能机器人学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对基于学习的操纵策略中动作预测与执行分离在刚性接触时的问题，提出URF统一框架，结合多模态观察预测虚拟目标等，利用接触力构建标签监督控制器模式预测，在相关任务中提升成功率并减少失败模式。
AI 中文摘要
基于学习的操纵策略通常从感官观察中预测机器人动作，并将执行交给单独的低级控制器。在刚性接触中，这种分离可能有问题，相同的虚拟目标运动或柔顺运动指令可能导致不稳定接触、跟踪误差、过度负载或工具损坏。本文提出统一机器人控制策略框架（URF），将柔顺动作预测与统一阻抗-导纳控制相连。给定多模态观察，URF预测虚拟目标、刚度矩阵和阻抗-导纳切换比率。通过测量接触力构建切换比率标签来监督控制器模式预测。在开箱和压线任务中，URF实现更高任务成功率，减少仅采用导纳执行时出现的失败模式。结果表明接触感知策略不仅预测柔顺动作，还预测执行它们的控制器行为有益。
英文摘要
Learning-based manipulation policies usually predict robot actions from sensory observations and leave their execution to a separate low-level controller. In rigid contact, this separation can be problematic: the same motion to a virtual target or compliant motion command can lead to unstable contact, tracking error, excessive loading, or tool damage, depending on the low-level controller. In this paper, we propose a \textit{Unified Robot Control-Policy Framework} (URF), which connects compliant action prediction with unified impedance-admittance control. Given multimodal observations, URF predicts a virtual target, a stiffness matrix, and an impedance-admittance switch ratio. The switch ratio determines when the controller should behave more like admittance control for accurate motion tracking and when it should move toward impedance control for safer rigid contact. Because demonstration data do not provide ground-truth environment stiffness, we construct switch-ratio labels from measured contact forces and use them to supervise controller-mode prediction. Across box-flipping and line-pressing tasks, URF achieves higher task success rates while reducing failure modes observed with admittance-only execution, including rapid force buildup, large force oscillations, tool breakage, and robot safety stops. These results suggest that contact-aware policies benefit from predicting not only compliant actions but also the controller behavior used to execute them. Project page: https://jiyou384.github.io/urf_project_page/
Comments8 pages, 5 figures, 2 tables. Submitted to IEEE Robotics and Automation Letters (RA-L)
