Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics
This work addresses the challenge of improving human understanding and robot control in periodic physical interactions, though it appears incremental as it builds on existing methods like VRNN and reservoir computing.
The paper tackles the problem of modeling periodic physical human-robot interaction by proposing a data-driven framework that modifies a variational recurrent neural network to explicitly include latent dynamics and integrates reservoir computing in the complex domain to represent periodic motions without amplitude decay. In a rope-rotation/swinging experiment, the framework achieved a well-distinguished latent state space and the best prediction accuracy for human observations and robot actions.
This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent developments of deep learning technologies would enable us to learn such a model from a dataset collected from the actual pHRI. Our framework is developed based on variational recurrent neural network (VRNN), which can inherently handle time-series data like one pHRI generates. This paper modifies VRNN in order to include the latent dynamics from robot to human explicitly. In addition, to analyze periodic motions like walking, we integrate a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons, with VRNN. By augmenting RC into complex domain, periodic behavior can be represented as the phase rotation in complex domain without decaying the amplitude. For verification of the proposed framework, a rope-rotation/swinging experiment was analyzed. The proposed framework, trained on the dataset collected from the experiment, achieved the latent state space where the differences in periodic motions can be distinguished. Such a well-distinguished space yielded the best prediction accuracy of the human observations and the robot actions. The attached video can be seen in youtube: https://youtu.be/umn0MVcIpsY