领域自适应的通信速率优化用于仿真到现实的人形机器人无线XR远程操作
Domain-Adaptive Communication-Rate Optimization for Sim-to-Real Humanoid-Robot Wireless XR Teleoperation
AI总结：
本文提出了一种领域自适应的通信速率优化方法，通过在仿真到现实的分布偏移中平衡重建误差和通信能耗，利用PAC-Bayes泛化特性分析和密度比加权的PPO方法，结合离线真实域数据校正，以提高人形机器人无线XR远程操作的通信效率和重建精度。
摘要：
无线扩展现实(XR)远程操作为收集人形机器人演示提供了具身交互能力，但大规模应用受到高频运动传输开销的限制。本文开发了一个系统框架，集成了采样、传输、插值和重建，并制定了通信速率优化，旨在通过维度采样率控制最小化通信能耗，同时保持机器人运动轨迹的重建精度。由于从物理机器人获取实时反馈受限于硬件成本，必须通过与离线真实域数据校正的仿真交互来解决问题。为了指导仿真到现实的适应，我们提供了一种PAC-Bayes泛化特性刻画，揭示了潜在密度比估计、有限样本偏差和编码器偏差的影响。基于此分析，我们提出了一种具有密度比加权和信任区域正则化的近端策略优化(PPO)方法。在公共人形远程操作数据集上的实验表明，所提出的方法在仿真到现实分布偏移中改善了重建误差和通信能耗之间的权衡。我们进一步分析了所提出算法在各种无线信道和动态运动轨迹中的有效性。
英文摘要：
Wireless extended reality (XR) teleoperation provides embodied interaction capability for collecting humanoid robot demonstrations, but the large-scale adoption is restricted by the overhead of high-frequency motion transmission. This paper develops a system framework that integrates sampling, transmission, interpolation, and reconstruction and formulates a communication-rate optimization that aims to minimize the communication energy while maintaining the reconstruction accuracy of robot motion trajectories through dimension-wise sampling-rate control. Since acquiring real-time feedback from physical robots is limited by hardware costs, it is necessary to solve the problem through simulator interaction with offline real-domain data correction. To guide sim-to-real adaptation, we provide a PAC-Bayes generalization characterization that reveals the effects of latent density-ratio estimation, finite-sample deviation, and encoder bias. Building on this analysis, we propose a proximal policy optimization (PPO) method with density-ratio weighting and trust-region regularization. Experiments on public humanoid teleoperation dataset show that the proposed method improves the tradeoff between reconstruction error and communication energy consumption under sim-to-real distribution shift. We further analyze the effectiveness of the proposed algorithm across various wireless channels and dynamic motion trajectories.
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。
