# Lessons Learned from Investigating Robotics-Based, Human-like Testing of an Upper-Body Exoskeleton
## 4 Lessons Learned
### Kinematics and Human-like Motion Generation  
Systematic testing of exoskeletons requires human-like movement capabilities. The kinematic structure of the robot must align with the specified workspace influenced by use-case requirements. However, our experiments revealed the challenge of achieving this with standard robot manipulators. Any form of connection between the robot and the exoskeleton establishes a parallel system, significantly constraining motion. This limitation is mediated when the exoskeleton is not rigidly attached to the robot, introducing deformations and additional degrees of freedom. These constraints must be carefully considered in platform design or motion planning, demanding extra effort to ensure accurate testing conditions.  
Every approach faced distinct limitations due to the inherent parallel nature of the interaction. For instance, if an anthropomorphic robotic arm is placed inside the exoskeleton, the ability to move the exoskeleton is restricted by the joints until the attachment point, as discussed in Section 3.1.2. This constraint necessitates having sufficient degrees of freedom to create motions closely resembling those a human would generate. Similarly, the \"Direct Attachment\" approach faces limitations as the joints before the attachment point define the available degrees of freedom to move the exoskeleton, potentially conflicting with the requirement for enough remaining degrees of freedom to interact with the environment.  
Additional challenges arise with the \"Human Arm Dummy\" approach. In addition to requiring human-like arm trajectories and satisfying constraints imposed by the parallel mechanism, controlling the sliding and motion of the arm within the arm shell introduces a significant control problem. Moreover, the generated motion must be constraint-consistent to prevent the generation of constraint forces that could compromise interaction measurements and protect the robotic hardware from exceeding limits. Existing human motion imitation methods for robots frequently do not address these complex environmental constraints. For instance, in [47] the motion imitation problem is formulated as an optimization problem and only considers hardware limits of the humanoid. As [48] pointed out in their review, constraints in general and avoidance of obstacles specifically is in general an under-researched problem and might only feature simple static and dynamical obstacles. Nevertheless, a dedicated robotic platform would need to account for all of these challenges, not only from the hardware point of view.  
A crucial insight gained from this exploration is the intricate connection between human-like motion generation, the design of the hardware platform, and the associated control challenges. It became evident that relying on a simplistic \"learn-from-the-human\" approach using standard methods like Dynamic Movement Primitives (DMP) falls short, highlighting the necessity for more advanced methods to address these complexities effectively. This motivates future research of special motion algorithms that need to account for the learned motion primitives and satisfy the constraints introduced by the coupling to the exoskeleton. Optimization-based algorithms could be a promising research direction to solve the motion retargeting problem in constrained environments, with the inherent challenge of maintaining the human-likeness of machine-learned motion primitives. The lesson learned is the importance of a nuanced and sophisticated approach to achieve the desired human-like motion in the testing context.