Simultaneously achieving low trajectory errors and compliant control explicit models of the task was effectively addressed with Compliant Movement Primitives (CMP). For a single-robot task, this means that it is accurately following its trajectory, but also exhibits compliant behavior in case of perturbations. In this paper we extend this kind of behavior without explicit models to bimanual tasks. In the presence of an external perturbation on any of the robots, they will both move in synchrony in order to maintain their relative posture, and thus not exert force on the object they are carrying. Thus, they will act compliantly in their absolute task, but remain stiff in their relative task. To achieve compliant absolute behavior and stiff relative behavior, we combine joint-space CMPs with the well known symmetric control approach. To reduce the necessary feedback reaction of symmetric control, we further augment it with copying of a virtual force vector at the end-effector, calculated through the measured external joint torques. Real-world results on two Kuka LWR-4 robots in a bimanual setting confirm the applicability of the approach.
This paper demonstrates a method for simultaneous transfer of positional and force requirements for in-contact tasks from a human instructor to a robotic arm through kinesthetic teaching. This is achieved by a specific use of the sensory configuration, where a force torque sensor is mounted between the tool and the flange of a robotic arm endowed with integrated torque sensors at each joint. The human demonstration is modeled using Dynamic Movement Primitives. Following human demonstration, the robot arm is provided with the capacity to perform sequential in-contact tasks, for example writing on a notepad a previously demonstrated sequence of characters. During the reenactment of the task, the system is not only able to imitate and generalize from demonstrated trajectories, but also from their associated force profiles. In fact, the implemented framework is extended to successfully recover from perturbations of the trajectory during reenactment and to cope with dynamic environments. Whole-body control in unknown environments is challenging: Unforeseen contacts with obstacles can lead to poor tracking performance and potential physical damages of the robot. Hence, a whole-body control approach for future humanoid robots in (partially) unknown environments needs to take contact sensing into account, e.g., by means of artificial skin. However, translating contacts from skin measurements into physically well-understood quantities can be problematic as the exact position and strength of the contact needs to be converted into torques. In this paper, we suggest an alternative approach that directly learns the mapping from both skin and the joint state to torques. We propose to learn such an inverse dynamics models with contacts using a mixture-of-contacts approach that exploits the linear superimposition of contact forces. The learned model can, making use of uncalibrated tactile sensors, accurately predict the torques needed to compensate for the contact. As a result, tracking of trajectories with obstacles and tactile contact can be executed more accurately. We demonstrate on the humanoid robot iCub that our approach improve the tracking error in presence of dynamic contacts. 