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In this paper, we propose a hybrid framework using visual and force sensing for human-robot co-carrying tasks. Visual sensing is utilized to obtain human motion and an observer is designed for estimating control input of human, which generates robot's desired motion towards human's intended motion. An adaptive impedance-based control strategy is proposed for trajectory tracking with neural networks (NNs) used to compensate for uncertainties in robot's dynamics. Motion synchronization is achieved and this approach yields a stable and efficient interaction behavior between human and robot, decreases human control effort and avoids interference to human during the interaction. The proposed framework is validated by a co-carrying task in simulations and experiments.

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Chenguang (2019) Bayesian estimation of human impedance and motion intention for humanrobot collaboration. IEEE Transactions on Cybernetics. pp. 1-13.

Yu (2019) Estimation of human impedance and motion intention for constrained human-robot interaction. Neurocomputing.

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