Joint Prediction of Human Motions and Actions in Human-Robot Collaboration
This addresses the need for robots to anticipate human intentions in collaborative settings, though it appears incremental as it builds on existing probabilistic methods.
The paper tackles the problem of jointly estimating and predicting human movements and actions for human-robot collaboration, introducing MA-HERP, a hierarchical probabilistic framework that shows accurate motion prediction and robust action inference under noise in preliminary experiments.
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.