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When a robot comanipulates an object with a human user, damping is a useful function. This is achieved by programming the robot to exhibit a viscous field. For some specific applications, the viscosity is required to change according to the manipulation velocity. A reported method is programming the viscosity varying inversely to the velocity. In this paper, this method is experimentally shown to distort human’s natural motion performance. This paper aims to propose a solution to solve this instability problem.

The authors performed a point-to-point targeting movement, where it was observed that the instability results from a sudden reduction of robot’s resistance to motion, which further results from the abrupt viscosity drop when the subject tries to accelerate. Therefore, the authors propose a solution where a first-order linear filter is added to the viscosity coefficient so as to slow down its variation.

The experimental results confirm that the proposition is effective, with the ability to stabilize the comanipulated dynamics and to restore the human’s natural behavior.

This paper concerns applications of comanipulation where the viscosity coefficient is designed to decrease as the velocity increases. An instability problem, which was of vital importance in terms of safety and performance but unreported in the literature, was experimentally studied through human–robot experiments. A solution was proposed by including a secondary dynamics in the variations of the viscosity. Its effectiveness was supported by the practical point-to-point motion experiments.

To promote the intuitiveness of collaborative tasks, the negotiation ability of humans with each other has inspired a large amount of studies aimed at reproducing the capacity in physical human-robot interaction (pHRI). This paper aims to promote mutual adaptation in negotiation when both parties possess incomplete information.

This paper introduces virtual fixtures into the traditional negotiation mechanism, locally regulating tracking trajectory and impedance parameters in the negotiating phase until the final plan integrates bilateral intentions well. In the strategy, robots convey its task information to humans and offer groups of guide plans for them to choose, on the premise of maximizing the robot’s own profits.

Compared with traditional negotiation strategies, humans adapt to robots easily and show lower cognitive load in the method, while the satisfied plan shows better performance for the whole human-robot system.

In this study, this paper proposes a novel negotiation strategy to facilitate the mutual adaptation of humans and robots in complicated shared tasks, especially when both parties possess incomplete information of tasks.