Indirect force control (IFC) architectures are a common approach for dealing with unknown environments. What all IFC schemes have in common is that the relation between the set point and the actual configuration of the robot is determined by a mechanical relationship (e.g., a mass-spring-damper system). In this paper, we propose a set-point generation method for IFC schemes, enabling intuitive specification of mixed force and positioning tasks on joint and Cartesian levels. In addition, the formulation of equality and inequality tasks is supported and a passivity-based stability proof is formulated using the concept of virtual energy storage. The resulting task programming interface is demonstrated on a 7-degree-of-freedom robot, running a joint space impedance controller. One sample task demonstrates the application of the developed approach and highlights the basic features.

In this work, we reformulate our previously developed strategy for operating unknown constrained mechanisms, to fit our new task specification framework for indirect force controlled robots, which we presented recently. The main improvement is a significant reduction of erroneous forces, which is achieved by breaking the manipulation task down into a hierarchical set of subtasks instead of having interfering tasks. The improvement is evaluated by conducting a series of manipulation experiments using the old and new approach and comparing the average erroneous forces.

In our previous work we derived a task specification approach for indirect force controlled robots to assign force and positioning tasks in joint and Cartesian space and execute them simultaneously in a hierarchical way. The virtual set points for an underlying joint space indirect force controller have been computed according to the specified tasks, supporting reactive control by generating virtual velocity commands.

The main contribution of this paper is the general formulation of force and positioning tasks on joint and Cartesian level for indirect force controlled robots and combining them in a strict hierarchical way. As a secondary contribution, we provide a simple and intuitive programming paradigm, using the developed formulation. By building on the well-established indirect force control scheme, which is often already provided for commercial robots, we provide application programmers with a useful tool for specifying tasks, involving positioning and force components. Different physical interaction tasks have been implemented to show the potential of the proposed method and discuss the general advantages and drawbacks.

In this paper we propose a set-point-generator (SPG) for indirect-force-controlled (IFC) manipulators, interacting with mechanisms, which impose narrow bilateral kinematic constraints on their end effector, like doors, cabinets and drawers. These mechanisms could also have dynamic properties, due to their inertia, friction and gravity, which demands to consider applied forces when choosing a set-point for the IFC. Neither the type of the constraint (linear, circular), nor required manipulation forces are assumed to be known. The proposed SPG consists of two parts: i) estimating the single direction of possible motion based on filtering of the measured end effector velocities; and ii) choosing an appropriate set-point for the underlying IFC, resulting in an effective operation of the mechanism. A major aspect of our approach is to explore the kinematic constraints with the manipulators desired set-point, avoiding direct force control, as the required interaction force is unknown and hence there is no definite reference force. The presented approach is a generalization of our previous work on constrained manipulation of unknown mechanisms and extends the applicability to a wider class of manipulators by considering joint-level IFC and taking into account the applied forces in yielding a robust and effective controller. The approach is evaluated in various experiments on a manipulator, providing joint space compliance.

The belief that artificial agents are useful interaction partners in cognitive therapies of social disorders such as autism fuels an increasing number of research projects involving the developments of robots and computer avatars. Yet, for an appropriate use of these new tools, it is necessary to understand how perception of and interaction with artificial agents differ from natural agents, both in normally developed adults and in patients with disorders of social cognition. Here we investigated the neural bases of social interactions with a human or with a humanoid robot using fMRI. During interaction, participants were playing a computerized version of the game chifumi (stone-paper-scissors), while believing they were interacting ‘live’ either with a fellow human (Intentional agent, Int), a humanoid robot endowed with an artificial intelligence (Artificial agent, Art), or a random number generator running on a laptop (Rnd). The belief was built both with an extensive briefing before the actual experiment, including a live interaction with the human and humanoid robot opponents, and with videos of the opponent, presented as live but actually recorded prior to the experiment, that preceded and followed each series of 5 games played against one opponent in a block. Results indicate that the brain network found when interacting with an active opponent (Art & Int vs Rnd) was more activated when interacting with the human than with the robot agent, implying that interacting with a human is more engaging than interacting with an artificial agent. Areas involved in social interactions in the posterior temporal sulcus were activated when controls, but not high-functioning autistic patients patients, interacted with a fellow human.

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.