Cooperative manipulation tasks can be divided into the subtasks of object trajectory tracking and grasp maintenance. Both subtasks typically pose individual requirements on the underlying control objective in terms of accuracy and robustness to disturbances. In this article, we propose a novel distributed impedance control scheme, resulting in a flexible control design to meet the individual-potentially conflicting-impedance goals of the respective subtasks. In order to achieve more efficient use of the communication resource, we propose an event-triggered strategy for the communication between the robotic agents. Robust, stability also in interaction with unknown objects and unknown environments, is guaranteed via passivity-based control design. Simulations and experimental results show that, with the proposed communication strategy, the number of transmissions between agents can be significantly reduced while maintaining the flexibility introduced by the distributed controller.

Knowing accurately the dynamic parameters of a manipulated object is required for common coordination strategies in physical human-robot interaction. Bias in object dynamics results in inaccurately calculated robot wrenches, which may disturb the human during interaction and bias the recognition of the human motion intention. This paper presents an identification strategy of object dynamics for physical human-robot interaction, which allows the tracking of desired human motion and inducing the motions necessary for parameter identification. The estimation of object dynamics is performed online and the estimator minimizes the least square error between the measured and estimated wrenches acting on the object. Identification-relevant motions are derived by analyzing the persistence of excitation condition, necessary for estimation convergence. Such motions are projected in the null space of the partial grasp matrix, relating the human and the robot redundant motion directions, to avoid disturbance of the human desired motion. The approach is evaluated in a physical human-robot object manipulation scenario.

Robot teams require planning and adaptive capabilities in order to perform cooperative manipulation tasks in dynamic or unstructured environments. Since these capabilities are inherent to humans, it is suitable to consider human-robot team teleoperation for cooperative manipulation where a single human collaborates with the robot team. In this paper, we present a subtask-based control approach which enables a simultaneous execution of two subtasks by the robot team, interacting with the object: trajectory tracking and formation preservation. Control inputs for both subtasks are provided by the human operator. The commands are projected onto the spaces of subtasks using a command mapping strategy. Analogously, measured interacting forces are projected onto the space of feedback signals, provided to the human via wearable fingertip haptic devices through a feedback mapping strategy. Experimental results validate the proposed approach.