Hierarchical Deep Learning for Intention Estimation of Teleoperation
  Manipulation in Assembly Tasks
Mingyu Cai Karankumar Patel Soshi Iba Songpo Li
In human-robot collaboration, shared control presents an opportunity to
teleoperate robotic manipulation to improve the efficiency of manufacturing and
assembly processes. Robots are expected to assist in executing the user's
intentions. To this end, robust and prompt intention estimation is needed,
relying on behavioral observations. The framework presents an intention
estimation technique at hierarchical levels i.e., low-level actions and
high-level tasks, by incorporating multi-scale hierarchical information in
neural networks. Technically, we employ hierarchical dependency loss to boost
overall accuracy. Furthermore, we propose a multi-window method that assigns
proper hierarchical prediction windows of input data. An analysis of the
predictive power with various inputs demonstrates the predominance of the deep
hierarchical model in the sense of prediction accuracy and early intention
identification. We implement the algorithm on a virtual reality (VR) setup to
teleoperate robotic hands in a simulation with various assembly tasks to show
the effectiveness of online estimation.