soconnor/honors-thesis
1
0
Fork 0
mirror of https://github.com/soconnor0919/honors-thesis.git synced 2026-05-08 07:08:55 -04:00
Code Issues Actions Packages Projects Releases 1 Activity

Add appendix on AI-assisted development workflow for HRIStudio

Browse Source 
This commit introduces a new appendix detailing the role of AI coding assistants in the development of HRIStudio. It covers the context of the project, tools used, division of responsibility, interaction patterns, and reflections on research integrity. The workflow is documented to provide transparency and insight into the development process, emphasizing the collaboration between human decisions and AI assistance.
This commit is contained in:
Sean O'Connor
2026-04-20 23:15:23 -04:00
parent 086b53880f
commit a7508c5698
14 changed files with 344 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files
thesis/chapters/01_introduction.tex
+1 -1
View File
@@ -9,7 +9,7 @@ To build the social robots of tomorrow, researchers must study how people respon
Social robotics, a subfield of HRI, focuses on robots designed for social interaction with humans, and it poses unique challenges for autonomy. In a typical social robotics interaction, a robot operates autonomously based on pre-programmed behaviors. Because human reactions to robot behaviors are not always predictable, pre-programmed autonomy often fails to respond appropriately to subtle social cues, causing the interaction to degrade.
To overcome this limitation, researchers use the WoZ technique. The name references L. Frank Baum's story \cite{Baum1900}, in which the "great and powerful" Oz is revealed to be an ordinary person operating machinery behind a curtain, creating an illusion of magic. In WoZ experiments, the wizard similarly creates an illusion of robot intelligence from behind the scenes. Consider a scenario where a researcher wants to test whether a robot tutor can effectively encourage student subjects during a learning task. Rather than building a complete autonomous system with speech recognition, natural language understanding, and emotion detection, the researcher may use a WoZ setup: a human operator (the ``wizard'') sits in a separate room, observing the interaction through cameras and microphones. When the subject appears frustrated, the wizard makes the robot say an encouraging phrase and perform a supportive gesture. To the subject, the robot appears to be acting autonomously, responding naturally to the subject's emotional state. This methodology allows researchers to rapidly prototype and test interaction designs, gathering valuable data about human responses before investing in the development of complex autonomous capabilities.
To overcome this limitation, researchers use the WoZ technique. The name references L. Frank Baum's story \cite{Baum1900}, in which the ``great and powerful'' Oz is revealed to be an ordinary person operating machinery behind a curtain, creating an illusion of magic. In WoZ experiments, the wizard similarly creates an illusion of robot intelligence from behind the scenes. Consider a scenario where a researcher wants to test whether a robot tutor can effectively encourage student subjects during a learning task. Rather than building a complete autonomous system with speech recognition, natural language understanding, and emotion detection, the researcher may use a WoZ setup: a human operator (the ``wizard'') sits in a separate room, observing the interaction through cameras and microphones. When the subject appears frustrated, the wizard makes the robot say an encouraging phrase and perform a supportive gesture. To the subject, the robot appears to be acting autonomously, responding naturally to the subject's emotional state. This methodology allows researchers to rapidly prototype and test interaction designs, gathering valuable data about human responses before investing in the development of complex autonomous capabilities.
Despite its versatility, WoZ research faces two critical challenges. The first is \emph{The Accessibility Problem}: many non-programmers, such as experts in psychology or sociology, may find it challenging to conduct their own studies without engineering support. The second is \emph{The Reproducibility Problem}: the hardware landscape is highly fragmented, and researchers frequently build custom control interfaces for specific robots and experiments. Because these tools are tightly coupled to particular hardware, running the same social interaction script on a different robot platform typically requires rebuilding the implementation from scratch. These tools are rarely shared, making it difficult for a researcher to reproduce the same study across different robot platforms or for other labs to replicate results.