This paper investigates goal-directed cooperative object swinging as a novel physical human-robot interaction scenario. We develop an energy-based control concept, which enables a robot to cooperate with a human in a goal-directed swing-up task. The robot can be assigned to be a leader or an actively contributing follower. We conduct a virtual reality experiment to compare effort sharing and performance of mixed human-human and human-robot dyads. The leader and the follower controllers yield similar results compared to their human standard.

Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. Our novel cognition-enabled control framework presented in this paper enables a robotic assistant to enrich its own experience by acquisition of human task knowledge during joint manipulation. Our robot incrementally learns semantic task structures during joint task execution using hierarchically clustered Hidden Markov Models. A semantic labeling of recognized task segments is acquired from the human partner through speech. After a small number of repetitions, the robot uses an anticipated task progress to generate a feed-forward set point for an admittance feedback control scheme. This paper describes the framework and its implementation on a mobile bi-manual platform. The evolution of the robot's task knowledge is presented and discussed. Finally, the cooperation quality is measured in terms of the robot's task contribution.

Synchronization occurs frequently in human behaviour: Everybody has experienced that in a group of people walking pace tends to equalize. The phenomenon of synchrony has been established in the literature in tasks which have little in common with daily life such as pendulum swinging and chair rocking. We extend the knowledge about human movement synchronization by showing that it also occurs during goal-directed actions. In a first experiment, we investigate how synchrony emerges develops over time. In a second experiment, we show that humans also synchronize their actions with a robot. Results are interpreted in the light of joint action theory. Possible implications and improvements for human-robot interaction are discussed.

Physical cooperation with humans greatly enhances the capabilities of robotic systems when leaving standardized industrial settings. In particular, manipulation of bulky objects in narrow environments requires cooperating partners. Actuation redundancies arising in joint manipulation impose the question of load sharing among the interacting partners. In this paper, effort sharing policies are systematically derived from the geometric and dynamic task properties. Three policies are intuitively identified, resulting in unilateral and balanced effort distributions. These policies are evaluated within a novel hierarchical motion generation and control framework. The synthesized system is successfully validated in a three-degrees-of-freedom planar tracking experiment. This evaluation shows an interdependency of the load sharing strategy and the resulting task performance.