# Telepresence Social Robotics towards Co-Presence: A Review
## 3 From Telepresence to Co-Presence Design
### User-Adaptive Systems Taxonomy
The user’s feedback reactions are used to continuously update robot knowledge and consequently tune the system’s behavior. Figure 6: General schematic of a user-adaptive system without the user’s model. The system’s behaviors are direct reactions to the user’s feedback, and decisions are made without the user’s previous knowledge. Figure 7: General schematic of a user-adaptive system based on static user models. The decision-making modules of the listed user-adaptive systems include different frameworks, such as the Markov decision process (MDP), partially observable Markov decision process (POMDP) (\(\alpha\)POMDP [195]), Mixed Observability Markov Decision Processes (MOMDP), fuzzy control, rule-based, hidden mode stochastic hybrid system (HMSHS), Bayes-adaptive, dynamic factor graph (DFG), active leaning, or reinforcement learning. Recent approaches for these frameworks are described in [196]. \begin{table}
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**Adaptive Parameters** & **Study** & **Input Modality** & **Decision Making** & **Output Modality** & **Evaluation Process** & **Evaluation Metrics** \\ \hline Robot’s Navigation Goal & [197] & Brain-actuated controls & Rule-based & Robot commands & Measurements, questionnaires, & Robot path \\ Decisions \(takeleftturn\) & [198] & Physical controls & POMDP & Image and sound & Measurements, questionnaires & POMDP rewards, perceived control, driving performance, similarity to real world, naturalness, social appropriateness \\ Robot Speed & [199] & User’s pose and speed & MOMDP & Motor control & Measurements & Speed difference and distance to the user \\ Decisions (object to move) & [200] & Speech, gaze & Rule-based & Robot arm movement & Measurements, questionnaires & Prediction accuracy, perceived awareness, response time and intentionality \\ Robot Speed & [201] & Ordered, Physical controls & Fuzzy control & motor controls & - & - \\ Decisions (warn driver or intervene) & [202] & Physical controls & Hidden mode stochastic hybrid system & Image and sound & Measurements & Time in unsafe and safe states \\ Decisions (room to clean) & [203] & User locations, task success & Motor control & Rule-based & - & - \\ Robot’s Navigation Goal & [204] & Physical controls & Rule-based & Robot commands & Measurements & Recognition accuracy \\ Voice Pitch & [205] & User speech & Rule-based & Robot speech & Questionnaires & Persistence and learning gain, rapport, perceived social presence \\ \hline \hline \end{tabular}
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Table 7: Adaptive parameters, input modalities, framework of decision, output modalities, and social robot evaluation with no user model. Figure 9: A schematic of the general architecture of a teleoperation system that includes an adaptive system. \begin{table}
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**Adaptive Parameters** & **Study** & **Input Modality** & **Decision Making** & **Output Modality** & **Evaluation Metrics** \\ \hline Robot’s Gestures and Speech & [210] & Speech & Rule-based & Gestures, speech & Questionnaires type of adaptation \\ Decisions (placement of objects) & [211] & Crowd-sourced data & Rule-based & Robot controls & Measurements & F-scores \\ Robot Location, Interface Complexity, Warning Levels, Font Size & [212] & - & Rule-based & - & - \\ Decisions (how to dress the user) & [213] & User’s pose, speech & Rule-based & Robot commands & Measurements & Task completion speed \\ Decisions (how to dress users) & [214] & User’s pose, speech & Rule-based & Robot commands & Measurements & Classification accuracy \\ Speech Output Gender, Sound Volume, Robot’s Name, Robot Speed & [215] & Speech, touch & Rule-based & & & & \\ Sequence of dance movements & [216] & User’s pose & Rule-based & Robot commands & Questionnaires type of adaptation \\ \hline \end{tabular}
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Table 8: Adaptive parameters, input modalities, framework of decision, output modalities and social robot evaluation with static user model.