## 6 Experimental Evaluation and Discussion  
The framework and the subordinate methods described in this paper for the assistance provision in the medication adherence scenario have been evaluated with the RAMCIP robot with 12 real participants diagnosed with MCI. The experiments took place in their own home environment, all located in Barcelona, Spain (Figure 14). Each participant had the opportunity to interact with the robot for at least seven days. During these days, the robot had been tested in several scenarios concerning assistive living; however, the results mentioned herein are focused on the medication scenario, which is the most important and complex one that includes all the developed robot skills. The overall findings of the experimentation are reported below along with discussion regarding the failure situations. It is worth mentioning that, before the actual interaction, one day had been spent for the robot's installation in each participant's house. The robot installation consisted of four mandatory steps including the metric map construction, its augmentation to obtain the hierarchical semantic properties, acquisition of the detailed 3D models for all small objects of interest, and collection of user specific information (e.g., medication schedule, facial features). In addition, during this phase, the user had the opportunity to be familiarized with the robot through a brief introduction to its functionalities and its communication capabilities. During the experimentation phase, in each house, it has been ensured that the robot's deployment will not be invasive to the the user's environment. Thus, the furniture topology has been kept intact apart from minor modifications in the area, so as to facilitate the robot's charging station and the parking position for the monitoring. In order to evaluate the performance of RAMCIP robot in the medication adherence scenario, two different methods were employed. The first concerned the documentation of each execution utilizing an external camera, and the second one concerned the development of a logging system integrated into the decision-making module and the task planner. The latter allowed the recording of the executed policy graph which tracked the robot's operation and registered the incidents that occurred from the ROS diagnostics. In this way, it has been ensured that both high level data, concerning the robot's decisions and selected actions (stemming from the POMDP), as well as low level information including failures from the implemented  
Figure 13: The POMDP and task planner integration schema. At the top, the causal relationships between POMDP states, actions, rewards, and observations are illustrated in accordance with [42] policy graph interpretation. At the bottom, the task planner functionality is illustrated, where each action is assigned to a specific task, which is further decomposed into ROS functionalities, i.e., action, service, node, which implements a specific robot \(Skill_{i}\). The outcome of each skill execution is coded and returned as observation \(\omega_{i}\) to the policy graph of the POMDP. skills, were monitored. The data extracted from this mechanism were compared with the ground truth (the external camera footage). In order to determine whether the scenario had been executed successfully, the robot after each repetition had to be able to assess with certainty if the user had indeed taken the medication or not. Overall, in compliance with the time schedule of the participant, the scenario was initiated proactively. During the seven days of engagement, this scenario was executed at least once per day, even if the medication schedule of the participant involved more than one session. In case the scenario was omitted for one day, due to either technical difficulties or participant's obligation to other activities, it was recompensed the next day by performing the scenario more than once.