让机器人边听边动，实时理解语言并调整动作
Incremental Language Understanding for Online Motion Planning of Robot Manipulators
- 用在线推理机制持续解析语音，动态更新动作规划
- 支持中途修改目标、约束或任务，无需重启动作
- 适合需要自然交互的机器人协作场景
人机交互要求机器人能够增量式处理语言，根据不断变化的语音输入实时调整行为。现有语言引导的机器人运动规划方法通常假设指令完整，导致在出现修正或澄清时需停止并重新规划，效率低下。本文提出一种基于推理的增量解析器，将在线运动规划算法嵌入认知架构中。该方法使机器人能持续适应动态语言输入，无需重启执行即可更新运动计划。增量解析器维护多个候选解析路径，利用推理机制解决歧义并在必要时修正理解。通过结合符号推理与在线运动规划，系统在处理语音修正和动态约束变化时展现出更强灵活性。我们在真实的人机交互场景中评估了该框架，展示了对目标位姿、约束条件或任务目标的在线调整能力。实验结果表明，将增量语言理解与实时运动规划结合，能显著提升人机协作的自然性与流畅性。相关演示视频见 www.acin.tuwien.ac.at/42d5。
原文摘要 · Abstract (English)
Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified instructions, resulting in inefficient stop-and-replan behavior when corrections or clarifications occur. In this paper, we introduce a novel reasoning-based incremental parser which integrates an online motion planning algorithm within the cognitive architecture. Our approach enables continuous adaptation to dynamic linguistic input, allowing robots to update motion plans without restarting execution. The incremental parser maintains multiple candidate parses, leveraging reasoning mechanisms to resolve ambiguities and revise interpretations when needed. By combining symbolic reasoning with online motion planning, our system achieves greater flexibility in handling speech corrections and dynamically changing constraints. We evaluate our framework in real-world human-robot interaction scenarios, demonstrating online adaptions of goal poses, constraints, or task objectives. Our results highlight the advantages of integrating incremental language understanding with real-time motion planning for natural and fluid human-robot collaboration. The experiments are demonstrated in the accompanying video at www.acin.tuwien.ac.at/42d5.
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