从反应到预见：通过代理任务图实现机器人操作的前瞻性故障恢复
From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation
AI总结：
本文提出AgentChord系统，通过代理任务图实现前瞻性故障恢复，提升机器人操作的可靠性和自主性。
摘要：
尽管机器人操作取得显著进展，但可靠执行仍具挑战，因任务失败在动态和无结构环境中不可避免。现有框架通常遵循检测-推理-恢复流程，导致高延迟和有限鲁棒性。受人类预见能力启发，本文引入AgentChord系统，将操作任务建模为有向任务图，并在执行前添加预见性恢复分支，以实现即时和针对性响应。AgentChord通过专门的代理编排：编曲者构建任务图，指挥者添加预见性恢复分支，指挥者编译并协调可执行转换，利用低延迟监控器检测偏差并触发预编译恢复，无需重新规划。实验表明，AgentChord显著提高了成功率和执行效率，推动了现实机器人系统的可靠性与自主性。项目页面：https://shengxu.net/AgentChord/
英文摘要：
Although robotic manipulation has made significant progress, reliable execution remains challenging because task failures are inevitable in dynamic and unstructured environments. To handle such failures, existing frameworks typically follow a stepwise detect-reason-recover pipeline, which often incurs high latency and limited robustness due to delayed reasoning and reactive planning. Inspired by the human capability to anticipate and proactively plan for potential failures, we introduce AgentChord, an agentic system that models a manipulation task as a directed task graph. Before execution, this graph is enriched with anticipatory recovery branches that specify context-aware corrective behaviors, enabling immediate and targeted responses when failures occur. Specifically, AgentChord operates through a choreography of specialized agents: a composer that structures the nominal task graph, an arranger that augments the graph with anticipatory recovery branches, and a conductor that compiles and coordinates executable transitions using low-latency monitors to detect deviations and trigger pre-compiled recoveries without re-planning. Empirical studies on diverse long-horizon bimanual manipulation tasks demonstrate that AgentChord substantially improves success rates and execution efficiency, advancing the reliability and autonomy of real-world robotic systems. The project page is available at: https://shengxu.net/AgentChord/.
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。
