Since strict separation of working spaces of humans and robots experiences a softening due to recent robotics research achievements, close interaction of humans and robots comes rapidly into reach. In this context, physical human-robot interaction raises a number of questions regarding a desired intuitive robot behavior. The continuous bilateral information and energy exchange requires an appropriate continuous robot feedback. Investigating a cooperative manipulation task, the desired behavior is a combination of an urge to fulfill the task, a smooth instant reactive behavior to human force inputs and an assignment of the task effort to the cooperating agents. In this paper, a formal analysis of human-robot cooperative load transport is presented. Three different possibilities for the assignment of task effort are proposed. Two proposed dynamic role exchange mechanisms adjust the robot's urge to complete the task based on the human feedback. For comparison, a static role allocation strategy not relying on the human agreement feedback is proposed as well. All three role allocation mechanisms are evaluated in a user study that involves large-scale kinesthetic interaction and full-body human motion. Results show tradeoffs between subjective and objective performance measures stating a clear objective advantage of the proposed dynamic role allocation scheme.

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 threedegrees-of-freedom planar tracking experiment. This evaluation shows an interdependency of the load sharing strategy and the resulting task performance.

This work aims at the development of a versatile control strategy for operating unknown mechanically constrained devices such as drawers or doors. Few assumptions on the device's shape as well as the utilized hardware are required. Our approach is based on an on-line estimation of the constraint manifold which serves as a reference input for an admittance-type controller providing the compliance required. The direction estimation is obtained from the velocity signal in task space. An on-line adaptation of the admittance controller according to the estimated moving direction reduces contact forces. The functionality of the control strategy is demonstrated on a mobile manipulator in a kitchen environment.

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Abstract-Goal-directed physical assistance to the human is one of the most challenging problems in the area of humanrobot interaction. Planning and learning from demonstration represent two conceptually different approaches to achieve goal-directed behavior. Here we examine the properties of a planning-based and a learning-based approach in the context of physical robotic assistance for the prototypical task of cooperative object maneuvering. In order to exploit the complementary strengths of planning and learning-based approaches we derive three novel synergy strategies. The algorithms are experimentally evaluated in a human user study in a planar virtual-reality scenario and in a proof-of-concept study with a human-sized mobile robot with two 7DoF arms. The results show that combinations of planning and learning algorithms are superior over the individual approaches.