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F. Corte Vargas
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Designing Emotionally Expressive Behaviors for an Appearance-Constrained Robot
Evaluating the Affective Interpretation of Motion, Light and Sound
How can robots without expressive faces or bodies convey emotions? Why would it be useful if robots could express emotion? In the context of human-robot interaction, could emotional expression lead to a greater comprehension of robotic behaviors and intents? These are questions addressed by the field of affective robotics, which seeks to develop and establish naturalistic social interaction between robots and humans. Emotions can provide a natural communication modality to augment the multi-modal capabilities of social robots in a variety of domains.
Historically, the emphasis in the field has been on facial and bodily expressions, relying heavily on anthropomorphic or zoomorphic robot appearances. This presents a challenge, as most robots are designed with functionality in mind, often lacking expressive faces and bodies, which limits their ability to effectively convey emotions. This study investigates the potential for appearance-constrained robots to convey emotions through variations in motion, light, and sound parameters.
We conducted an experiment where participants rated the emotional qualities of a non-humanoid, faceless robot’s behaviors, which were manipulated through variations in motion, light, and sound parameters. Our approach is unique in that it adopts a bottom-up methodology similar to the work of Jack et al. on facial expressions. By systematically varying individual features and observing the resultant emotional perceptions, we aimed to discern the specific affective contributions of each parameter. Using machine-learning based regression models, we sought to predict the perceived emotional qualities based on these systematically varied parameters.
Our findings reveal that variations in motion parameters, particularly speed, significantly influence the perceived intensity of arousal, joy, and dominance. Light temperature was found to affect the perceived intensity of anger and joy, while sound pitch influenced perceptions of surprise and fear. The regression models showed varying degrees of success, with the random forest models often outperforming linear models but also exhibiting a higher tendency to overfit the training data. The linear models, while less prone to overfitting, struggled to capture the full complexity of the emotional responses. These findings suggest that non-anthropomorphic robots can indeed convey emotional qualities through controlled variations in their behaviors, though the strength and clarity of these emotions remain limited. Future research should focus on enhancing the expressiveness of these parameters and testing the models with new data to better understand their generalizability and effectiveness. ...
Historically, the emphasis in the field has been on facial and bodily expressions, relying heavily on anthropomorphic or zoomorphic robot appearances. This presents a challenge, as most robots are designed with functionality in mind, often lacking expressive faces and bodies, which limits their ability to effectively convey emotions. This study investigates the potential for appearance-constrained robots to convey emotions through variations in motion, light, and sound parameters.
We conducted an experiment where participants rated the emotional qualities of a non-humanoid, faceless robot’s behaviors, which were manipulated through variations in motion, light, and sound parameters. Our approach is unique in that it adopts a bottom-up methodology similar to the work of Jack et al. on facial expressions. By systematically varying individual features and observing the resultant emotional perceptions, we aimed to discern the specific affective contributions of each parameter. Using machine-learning based regression models, we sought to predict the perceived emotional qualities based on these systematically varied parameters.
Our findings reveal that variations in motion parameters, particularly speed, significantly influence the perceived intensity of arousal, joy, and dominance. Light temperature was found to affect the perceived intensity of anger and joy, while sound pitch influenced perceptions of surprise and fear. The regression models showed varying degrees of success, with the random forest models often outperforming linear models but also exhibiting a higher tendency to overfit the training data. The linear models, while less prone to overfitting, struggled to capture the full complexity of the emotional responses. These findings suggest that non-anthropomorphic robots can indeed convey emotional qualities through controlled variations in their behaviors, though the strength and clarity of these emotions remain limited. Future research should focus on enhancing the expressiveness of these parameters and testing the models with new data to better understand their generalizability and effectiveness. ...
How can robots without expressive faces or bodies convey emotions? Why would it be useful if robots could express emotion? In the context of human-robot interaction, could emotional expression lead to a greater comprehension of robotic behaviors and intents? These are questions addressed by the field of affective robotics, which seeks to develop and establish naturalistic social interaction between robots and humans. Emotions can provide a natural communication modality to augment the multi-modal capabilities of social robots in a variety of domains.
Historically, the emphasis in the field has been on facial and bodily expressions, relying heavily on anthropomorphic or zoomorphic robot appearances. This presents a challenge, as most robots are designed with functionality in mind, often lacking expressive faces and bodies, which limits their ability to effectively convey emotions. This study investigates the potential for appearance-constrained robots to convey emotions through variations in motion, light, and sound parameters.
We conducted an experiment where participants rated the emotional qualities of a non-humanoid, faceless robot’s behaviors, which were manipulated through variations in motion, light, and sound parameters. Our approach is unique in that it adopts a bottom-up methodology similar to the work of Jack et al. on facial expressions. By systematically varying individual features and observing the resultant emotional perceptions, we aimed to discern the specific affective contributions of each parameter. Using machine-learning based regression models, we sought to predict the perceived emotional qualities based on these systematically varied parameters.
Our findings reveal that variations in motion parameters, particularly speed, significantly influence the perceived intensity of arousal, joy, and dominance. Light temperature was found to affect the perceived intensity of anger and joy, while sound pitch influenced perceptions of surprise and fear. The regression models showed varying degrees of success, with the random forest models often outperforming linear models but also exhibiting a higher tendency to overfit the training data. The linear models, while less prone to overfitting, struggled to capture the full complexity of the emotional responses. These findings suggest that non-anthropomorphic robots can indeed convey emotional qualities through controlled variations in their behaviors, though the strength and clarity of these emotions remain limited. Future research should focus on enhancing the expressiveness of these parameters and testing the models with new data to better understand their generalizability and effectiveness.
Historically, the emphasis in the field has been on facial and bodily expressions, relying heavily on anthropomorphic or zoomorphic robot appearances. This presents a challenge, as most robots are designed with functionality in mind, often lacking expressive faces and bodies, which limits their ability to effectively convey emotions. This study investigates the potential for appearance-constrained robots to convey emotions through variations in motion, light, and sound parameters.
We conducted an experiment where participants rated the emotional qualities of a non-humanoid, faceless robot’s behaviors, which were manipulated through variations in motion, light, and sound parameters. Our approach is unique in that it adopts a bottom-up methodology similar to the work of Jack et al. on facial expressions. By systematically varying individual features and observing the resultant emotional perceptions, we aimed to discern the specific affective contributions of each parameter. Using machine-learning based regression models, we sought to predict the perceived emotional qualities based on these systematically varied parameters.
Our findings reveal that variations in motion parameters, particularly speed, significantly influence the perceived intensity of arousal, joy, and dominance. Light temperature was found to affect the perceived intensity of anger and joy, while sound pitch influenced perceptions of surprise and fear. The regression models showed varying degrees of success, with the random forest models often outperforming linear models but also exhibiting a higher tendency to overfit the training data. The linear models, while less prone to overfitting, struggled to capture the full complexity of the emotional responses. These findings suggest that non-anthropomorphic robots can indeed convey emotional qualities through controlled variations in their behaviors, though the strength and clarity of these emotions remain limited. Future research should focus on enhancing the expressiveness of these parameters and testing the models with new data to better understand their generalizability and effectiveness.
Arcadian Renewable Energy System
Renewable Energy for Mars Habitat
Bachelor thesis
(2020)
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F. Corte Vargas, M. Géczi, S. Heidweiller, M.X. Kempers, B.J. Klootwijk, F. van Marion, D. Mordasov, L.H. Ouroumova, E.N. Terwindt, D. Witte, R. Schmehl, D.A. von Terzi, B.V.S. Jyoti, C. Belo Gomes Brito
The human eye has turned itself back to the sky with the commercialisation of the space industry, and a new goal has been set. Setting foot on the Red Planet is the next stage of the human exploration of the universe. The travel to Mars is very lengthy and costly, nonetheless the planet still shows great potential for sustaining human life. To make this a possibility, there is a need for locally sourced energy. The presence of (re-)usable resources on Mars could pave the way to further expand the exploration to an interplanetary scale, and successfully maintain a human presence outside the Earth's atmosphere. The availability of energy will be a key indicator for the success of the human race in the colonisation of Mars. To answer this call for the need to generate locally sourced energy, the design of a renewable energy system was started by a team of students and staff from the faculty of Aerospace Engineering at Delft University of Technology: The Arcadian Renewable Energy System (ARES). The energy system will power the construction and operations of a Mars habitat, to support the livability of humans. The system will use complementary renewable energy sources integrated into a microgrid, to sustainably harvest energy from local Martian environment and resources. To ensure the design will be able to fulfil its purpose, a mission need statement and a project objective statement are generated: Mission Need Statement: To provide renewable energy supply of 10 kW to a Mars habitat. Project Objective Statement: Design a renewable energy supply system, primarily focusing on wind energy, which provides 10kW to a Mars habitat, by 10 students in 10 weeks. Synthesis Exercise (DSE) will last a total of 10 weeks, beginning on the 20th of April, ending on the 2nd of July, with a poster session and symposium. The DSE is in collaboration with the Architectural faculty, where a separate team of students is working on a rhizomatic Mars habitat project as part of an ESA competition, which has an ESA-ESTEC feasibility study proposal incorporated. Due to the multi-disciplinary nature of this project, it is important that the DSE team produces a complete and verified design as the outcome. The Design The design the DSE has come up with consists of two energy production systems, namely the primary and secondary energy system providing wind and solar energy, respectively. In addition the system also consists of a power management and energy storage system.
...
The human eye has turned itself back to the sky with the commercialisation of the space industry, and a new goal has been set. Setting foot on the Red Planet is the next stage of the human exploration of the universe. The travel to Mars is very lengthy and costly, nonetheless the planet still shows great potential for sustaining human life. To make this a possibility, there is a need for locally sourced energy. The presence of (re-)usable resources on Mars could pave the way to further expand the exploration to an interplanetary scale, and successfully maintain a human presence outside the Earth's atmosphere. The availability of energy will be a key indicator for the success of the human race in the colonisation of Mars. To answer this call for the need to generate locally sourced energy, the design of a renewable energy system was started by a team of students and staff from the faculty of Aerospace Engineering at Delft University of Technology: The Arcadian Renewable Energy System (ARES). The energy system will power the construction and operations of a Mars habitat, to support the livability of humans. The system will use complementary renewable energy sources integrated into a microgrid, to sustainably harvest energy from local Martian environment and resources. To ensure the design will be able to fulfil its purpose, a mission need statement and a project objective statement are generated: Mission Need Statement: To provide renewable energy supply of 10 kW to a Mars habitat. Project Objective Statement: Design a renewable energy supply system, primarily focusing on wind energy, which provides 10kW to a Mars habitat, by 10 students in 10 weeks. Synthesis Exercise (DSE) will last a total of 10 weeks, beginning on the 20th of April, ending on the 2nd of July, with a poster session and symposium. The DSE is in collaboration with the Architectural faculty, where a separate team of students is working on a rhizomatic Mars habitat project as part of an ESA competition, which has an ESA-ESTEC feasibility study proposal incorporated. Due to the multi-disciplinary nature of this project, it is important that the DSE team produces a complete and verified design as the outcome. The Design The design the DSE has come up with consists of two energy production systems, namely the primary and secondary energy system providing wind and solar energy, respectively. In addition the system also consists of a power management and energy storage system.