发表机构
Cognitive Neurorobotics Research Unit; Okinawa Institute of Science and Technology Graduate University（认知神经机器人学研究单元; 冲绳科学技术大学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于预测编码的机制，利用累积重构误差在线调节元先验，在物理人机交互中平衡内部预测与外部感觉，实验表明可减少预测误差和交互冲突。
AI 中文摘要
预测编码将自适应行为描述为内部生成的预测与外部感觉证据之间的动态平衡，然而，一个具身认知系统如何在持续交互过程中在线调节这种平衡，仍知之甚少。本研究提出了一种基于预测编码的机制，用于在物理人机交互过程中调节内部生成与外部耦合的加工。该框架采用受预测编码启发的变分递归神经网络（PV-RNN），其中元先验控制后验推断受学习到的先验动力学约束的程度。我们通过一种在线机制扩展了该架构，该机制利用近期交互历史中累积的重构误差在预定义的元先验模式之间进行选择。该机制在三个物理人机交互任务中进行了评估，涉及固定结构化、变化结构化和较少约束的交互。在所有任务中，较低的元先验值产生了预期的后验-先验散度增加和重构误差减少。更重要的是，重构历史驱动的模式选择还与前瞻性预测误差和机器人侧物理交互冲突的减少相关，表明其影响超出了回顾性重构目标本身。任务3进一步表明，最近的感官观察可以被成功容纳，而随后的运动仍偏离模型先验生成的未来轨迹。总体而言，这些发现表明，累积的重构不匹配可以提供一种内源性信号，用于调节在具身交互过程中后续推断相对于持续感觉输入依赖所学内部动力学的程度。
英文摘要
Predictive coding characterizes adaptive behavior as a dynamic balance between internally generated predictions and external sensory evidence, yet how an embodied cognitive system can regulate this balance online during ongoing interaction remains poorly understood. This study proposes a predictive-coding-based mechanism for regulating internally generated and externally coupled processing during physical human--robot interaction. The framework employs a predictive-coding-inspired variational recurrent neural network (PV-RNN), in which a meta-prior controls the degree to which posterior inference is constrained by learned prior dynamics. We extend this architecture with an online mechanism that uses reconstruction error accumulated over recent interaction history to select between predefined meta-prior regimes. The mechanism was evaluated across three physical human--robot interaction tasks involving fixed structured, changing structured, and less-constrained interaction. Across all tasks, lower meta-prior values produced the expected increase in posterior--prior divergence and reduction in reconstruction error. More importantly, reconstruction-history-driven regime selection was also associated with reduced prospective prediction error and robot-side physical interaction conflict, demonstrating consequences beyond the retrospective reconstruction objective itself. Task~3 further showed that recent sensory observations can be successfully accommodated while subsequent human motion still departs from the model's prior-generated future trajectory. Overall, these findings show that accumulated reconstruction mismatch can provide an endogenous signal for regulating how strongly subsequent inference relies on learned internal dynamics relative to ongoing sensory input during embodied interaction.
Comments16 pages, 8 figures. Submitted to IEEE Transactions on Cognitive and Developmental Systems
