Intention inference can be an essential step toward efficient human-robot interaction. For this purpose, we propose the Intention-Driven Dynamics Model (IDDM) to probabilistically model the generative process of movements that are directed by the intention. The IDDM allows the intention to be inferred from observed movements using Bayes' theorem. The IDDM simultaneously finds a latent state representation of noisy and high-dimensional observations, and models the intention-driven dynamics in the latent states. As most robotics applications are subject to real-time constraints, we develop an efficient online algorithm that allows for real-time intention inference. Two human-robot interaction scenarios, i.e. target prediction for robot table tennis and action recognition for interactive humanoid robots, are used to evaluate the performance of our inference algorithm. In both intention inference tasks, the proposed algorithm achieves substantial improvements over support vector machines and Gaussian processes.
Wang et al. REF propose the Intention-Driven Dynamics Model (IDDM) as a probabilistic graphical model with observations, latent states and intentions where the transitions between latent states and the mapping from latent states to observations are modeled as Gaussian Processes.
Probabilistic movement modeling for intention inference in human–robot interaction
The International Journal of Robotics Research
Computer Science