Fluent human--robot collaboration requires robots to continuously estimate human behaviour and anticipate future intentions. This entails reasoning jointly about \emph{continuous movements} and \emph{discrete actions}, which are still largely modelled in isolation. In this paper, we introduce \textsf{MA-HERP}, a hierarchical and recursive probabilistic framework for the \emph{joint estimation and prediction} of human movements and actions. The model combines: (i) a hierarchical representation in which movements compose into actions through admissible Allen interval relations, (ii) a unified probabilistic factorisation coupling continuous dynamics, discrete labels, and durations, and (iii) a recursive inference scheme inspired by Bayesian filtering, alternating top-down action prediction with bottom-up sensory evidence. We present a preliminary experimental evaluation based on neural models trained on musculoskeletal simulations of reaching movements, showing accurate motion prediction, robust action inference under noise, and computational performance compatible with on-line human--robot collaboration.

翻译：流畅的人机协作要求机器人持续估计人类行为并预判未来意图。这需要对**连续运动**与**离散动作**进行联合推理，而现有研究多将两者独立建模。本文提出MA-HERP——一种面向人体运动与动作**联合估计与预测**的层次化递归概率框架。该模型融合了：(i) 层次化表征——通过可采纳的Allen区间关系使运动组合为动作；(ii) 统一概率分解——耦合连续动态、离散标签与持续时间；(iii) 递归推断方案——受贝叶斯滤波启发，交替执行自上而下的动作预测与自下而上的感官证据更新。我们基于肌肉骨骼仿真中的抓取运动神经模型进行了初步实验评估，结果表明该方法能实现精准的运动预测、在噪声条件下的鲁棒动作推断，并具备支持在线人机协作的计算性能。