Passivity-based control is a cornerstone of control theory and an established design approach in robotics. Its strength is based on the passivity theorem, which provides a powerful interconnection framework for robotics. However, the design of passivity-based controllers and their optimal tuning remain challenging. We propose here an intuitive design approach for fully actuated robots, where the control action is determined by a ‘virtual-mechanism’ as in classical virtual model control. The result is a robot whose controlled behavior can be understood in terms of physics. We achieve optimal tuning by applying algorithmic differentiation to ODE simulations of the rigid body dynamics. Overall, this leads to a flexible design and optimization approach: stability is proven by passivity of the virtual mechanism, while performance is obtained by optimization using algorithmic differentiation.

Collaborative robots (cobots) are a promising technology for frontline workers in industry. They can support tasks that cannot be fully automated but are repetitive, fatiguing, boring, or dangerous for humans. Although cobots are explicitly designed to work with humans, they remain primarily non-intuitive and difficult to collaborate with. Thus, there is a need for new interaction approaches to facilitate efficient human-robot collaboration. Recently, we could see emerging examples of using augmented reality (AR) to assist a worker in collaborative task execution with a cobot. However, for such an approach to provide truly efficient support for the seamless bimanual task execution, we need to first investigate interaction methods offered by an AR interface. To that end, we performed a study with sixteen participants to compare eye-gaze and hand-ray pointing methods for part selection in collaborative, manual assembly tasks. The results of our study show that both techniques provide similar perceived usability, with the eye-gaze selection leading to significantly shorter completion times.

Virtual Model Control (VMC) is an approach to design a controller for force-controlled robots in complex uncertain environments. While this method was primarily investigated for legged robot locomotion in the past, it can be more generally applicable to other types of robotic systems. This paper investigates the VMC framework for reaching tasks in a force-controlled robotic arm. We propose six different approaches to designing virtual models in order to achieve reaching tasks in environments with obstacles and uncertainties. A force-controlled 8 degree-of-freedom humanoid robot was used to validate the proposed approach in the real world. We conducted three experiments to test the performance of VMC controllers in terms of predictability, sensitivity to external force, and adaptability against known and unknown obstacles. Experimental analyses show that, even though the proposed approach needs to sacrifice accuracy and trajectory optimality, it enables us to design complex reaching motions under uncertainties, in an intuitive and extendable manner.