Assistive devices like exoskeletons undergo extensive testing not least because of their close interaction with humans. Conducting user studies is a time-consuming process that demands expert knowledge, and it is accompanied by challenges such as low repeatability and a potential lack of comparability between studies. Obtaining objective feedback on the exoskeleton’s performance is crucial for developers and manufacturers to iteratively improve the design and development process. This paper contributes to the concept of using robots for objective exoskeleton testing by presenting various approaches to a robotic-based testing platform for upper-body exoskeletons. We outline the necessary requirements for realistically simulating use cases and evaluate different approaches using standard manipulators as robotic motion generators. Three approaches are investigated: (i) Exploiting the anthropomorphic structure of the robotic arm and directly placing it into the exoskeleton. (ii) Utilizing a customized, direct attachment between the robot and exoskeleton. (iii) Attaching a human arm dummy to the robot end effector to simulate a more realistic interface with the exoskeleton. Subsequently, we discuss and compare the results against the aforementioned requirements of a systematic testing platform. Our conclusion emphasizes that achieving objective and realistic testing necessitates highly specialized hardware, algorithms, and further research to address challenging requirements.

Deformable linear objects (DLOs) exhibit highly nonlinear dynamic behavior, complicating their control during high-speed maneuvers. Furthermore, the lack of a generic spatial representation and the difficulty of real-time state estimation hinder effective closed-loop manipulation. Building upon our previously established state estimation framework and control architecture, this paper introduces a constrained, closed-loop model predictive control (MPC) approach for highly dynamic DLO manipulation. We integrate hardware and environmental limits into the MPC as soft constraints, enabling tasks like dynamic tracking with endpoint wall avoidance. Additionally, we demonstrate challenging maneuvers, such as swinging a DLO through a narrow slot in open-loop. Furthermore, we enhance our prior state estimation method by incorporating mid-object via points, significantly improving shape reconstruction and enabling the prediction of non-monotonically curved geometries. Finally, we present an extensive evaluation of the complete control architecture using two simulated and five physical DLOs. This analysis assesses the critical influence of kinematic segment discretization, object dynamics, and distribution shifts on overall tracking performance, thereby validating the efficacy and robustness of the approach.