接触导向策略：具备生成接触基础的灵活视觉-触觉策略
Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding
AI总结：
本文提出Contact-Grounded Policy，通过预测机器人状态和触觉反馈的耦合轨迹，生成接触基础的灵活视觉-触觉策略，优于现有基线方法。
摘要：
在多指手的接触丰富灵巧操作中，任务成功依赖于持续演变的多点接触，对物体几何、摩擦转换和滑动高度敏感。最近，触觉指导的操作策略显示出潜力。然而，大多数方法将触觉信号作为附加观测，而非建模接触状态或动作输出与低层控制器动力学的交互。本文提出Contact-Grounded Policy (CGP)，一种视觉-触觉策略，通过预测实际机器人状态和触觉反馈的耦合轨迹，将多点接触接地，并利用学习的接触一致性映射将这些预测转换为可执行的目标机器人状态，供合规控制器使用。CGP由两个组件组成：(i) 一个条件扩散模型，用于在压缩潜在空间中预测未来的机器人状态和触觉反馈；(ii) 一个学习的接触一致性映射，将预测的机器人状态-触觉对转换为合规控制器的可执行目标，使其能够实现预期的接触。我们使用物理的四指Allegro V5手和Digit360指尖触觉传感器，以及模拟的五指Tesollo DG-5F手和密集的全手触觉阵列评估CGP。在一系列灵巧任务中，包括在手内操作、精细抓取和工具使用，CGP优于视觉-运动和视觉-触觉扩散策略基线。
英文摘要：
Contact-rich dexterous manipulation with multi-finger hands remains an open challenge in robotics because task success depends on multi-point contacts that continuously evolve and are highly sensitive to object geometry, frictional transitions, and slip. Recently, tactile-informed manipulation policies have shown promise. However, most use tactile signals as additional observations rather than modeling contact state or how their action outputs interact with low-level controller dynamics. We present Contact-Grounded Policy (CGP), a visuotactile policy that grounds multi-point contacts by predicting coupled trajectories of actual robot state and tactile feedback, and using a learned contact-consistency mapping to convert these predictions into executable target robot states for a compliance controller. CGP consists of two components: (i) a conditional diffusion model that forecasts future robot state and tactile feedback in a compressed latent space, and (ii) a learned contact-consistency mapping that converts the predicted robot state-tactile pair into executable targets for a compliance controller, enabling it to realize the intended contacts. We evaluate CGP using a physical four-finger Allegro V5 hand with Digit360 fingertip tactile sensors, and a simulated five-finger Tesollo DG-5F hand with dense whole-hand tactile arrays. Across a range of dexterous tasks including in-hand manipulation, delicate grasping, and tool use, CGP outperforms visuomotor and visuotactile diffusion-policy baselines.
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。
