FutureRTC：基于预期条件动作分块的实时机器人执行
FutureRTC: Real-Time Robot Execution with Anticipatory-Conditioned Action Chunking
AI总结：
研究视觉-语言-动作策略实时部署中异步执行的问题，提出FutureRTC框架，通过状态校正和观测预测模块及策略一致性损失，无需修改底层策略，有效提升对推理延迟的鲁棒性，实现更优轨迹、执行速度和任务成功率。
AI中文摘要：
视觉-语言-动作（VLA）策略的实时部署需要异步执行，这会导致预测-执行失准，表现为块间不连续。现有方法存在不足。本文提出FutureRTC，一个即插即用的适配框架，无需修改底层策略就能预测异步VLA控制的执行时间观测和状态。它有状态校正模块和观测预测模块，还引入策略一致性损失。实验表明，FutureRTC对推理延迟具有卓越鲁棒性，能实现更平滑轨迹、更快执行和更高任务成功率。
英文摘要：
Real-time deployment of Vision-Language-Action (VLA) policies necessitates asynchronous execution, wherein subsequent action chunks are computed concurrently with the execution of the current chunk, leading to prediction-execution misalignment and manifesting as inter-chunk discontinuities. Existing methods either superficially smooth chunk boundaries, require costly policy optimization, or exclusively forward-predict proprioceptive states yet neglect critical visual observations. In this paper, we propose \textbf{FutureRTC}, a plug-and-play adaptation framework that predicts execution-time observations and states for asynchronous VLA control without modifying the underlying policy. Specifically, FutureRTC features a state correction module to compensate for the discrepancy between rolled-forward and actual execution-time proprioceptive states and an observation prediction module that forecasts execution-time visual representations by leveraging robot motion as an explicit physical prior through motion-aware feature transport and reconstruction. Furthermore, we introduce a policy consistency loss to align the action chunks generated from predicted contexts with those produced under the expected execution-time inputs of the VLA policy. Extensive experiments across simulated and real-world environments demonstrate that FutureRTC achieves superior robustness to inference delays, resulting in smoother trajectories, faster execution, and consistently higher task success rates.
