HIRE：基于历史条件的交互推理与高速执行用于视觉混淆的精密操作
HIRE: History-Conditioned Interaction Reasoning and High-Rate Execution for Visually Aliased Precision Manipulation
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- MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统实验室）
- School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
- Zhongguancun Academy（中关村学院）
- Noematrix
- Shanghai Jiao Tong University（上海交通大学）
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中文总结 AI 辅助
针对视觉混淆下精密操作的历史依赖问题，提出HIRE跨速率框架，结合历史条件推理器与高速执行器，在真实机器人任务中达到90%以上完成率。
中文摘要 AI 辅助
具有接触关键交互的精密操作通常是历史依赖的：视觉上相似的观测可能对应不同的潜在交互状态，因此需要不同的动作，而小的执行误差可能改变任务结果。仅依赖当前视觉观测的策略无法解决这种歧义；力感知和记忆增强方法丰富了物理或时间上下文，而反应式高速策略改善了局部接触响应，然而长时程时间推理与精密执行在现有方法中仍然基本解耦，限制了在视觉混淆的精密操作中的可靠进展。为弥合这一差距，我们提出了历史条件交互推理与执行（HIRE），一个跨速率框架，包含历史条件交互状态推理器（ISR）和高速交互流形执行器（IME）。ISR使用时间力编码器和力感知器编码有序力历史，作为状态一致动作生成的持久物理证据，而IME将接触关键运动结构化为内在进展和横向校正以实现精密执行；它们的跨速率循环允许产生的物理痕迹为后续推理提供信息。在跨表面、插入和旋转交互的真实机器人实验中，HIRE在所有评估任务阶段达到至少90%的完成率，同时改善了交互状态消歧、执行精度和泛化能力。更广泛地，HIRE为历史依赖部分可观测性下的精密操作提供了统一的推理-执行视角，其中物理交互既实现任务意图又揭示潜在状态证据以供未来决策。代码将在发表后发布。
英文摘要
Precision manipulation with contact-critical interactions is often history-dependent: visually similar observations can correspond to different latent interaction states and therefore require different actions, while small execution errors can alter task outcomes. Policies relying on the current visual observation alone cannot resolve such ambiguity; force-aware and memory-augmented methods enrich physical or temporal context, while reactive high-rate policies improve local contact response, yet long-horizon temporal reasoning and precision execution remain largely decoupled in existing methods, limiting reliable progression in visually aliased precision manipulation. To bridge this gap, we introduce History-Conditioned Interaction Reasoning and Execution (HIRE), a cross-rate framework comprising a history-conditioned Interaction-State Reasoner (ISR) and a high-rate Interaction-Manifold Executor (IME). ISR encodes ordered wrench history with a temporal wrench encoder and Force Perceiver as persistent physical evidence for state-consistent action generation, while IME structures contact-critical motion into intrinsic progress and transverse correction for precise execution; their cross-rate loop allows the resulting physical traces to inform subsequent reasoning. In real-robot experiments across surface, insertion, and rotational interactions, HIRE achieves at least 90% completion across all evaluated task stages while improving interaction-state disambiguation, execution precision, and generalization. More broadly, HIRE provides a unified reasoning--execution perspective on precision manipulation under history-dependent partial observability, where physical interaction both realizes task intent and reveals latent-state evidence for future decisions. Code will be released upon publication.
