https://sayedcseku.github.io/tags/user-perception-study/User Perception StudyHugo Blox Builder (https://hugoblox.com)en-usMon, 01 Sep 2025 00:00:00 +0000https://sayedcseku.github.io/media/icon_hu_bf4b945a42c519b1.pnghttps://sayedcseku.github.io/tags/user-perception-study/https://sayedcseku.github.io/project/role-shifting-hri/Mon, 01 Sep 2025 00:00:00 +0000https://sayedcseku.github.io/project/role-shifting-hri/<p><strong>Role Shifting in Human–Robot Collaboration (Fall 2025)</strong> investigates embodied communication inspired by American Sign Language (ASL), utilizing torso-rotation perspective-taking behaviors on a Unitree G1 humanoid robot to improve referential clarity in collaborative handover and courier tasks.</p> <h2 id="key-contributions">Key Contributions</h2> <ul> <li><strong>Embodied Perspective-Taking</strong>: Implemented torso-based role shifting inspired by American Sign Language to improve gesture clarity and disambiguation in human–robot interaction.</li> <li><strong>Comparative Human Perception Study</strong>: Evaluated gesture-only versus torso-rotation communication behaviors in user perception experiments, quantifying communicative transparency, naturalness, and trust.</li> <li><strong>Evaluation Metrics</strong>: Designed metrics to assess clarity, naturalness, and perceived collaborative intelligence in HRI.</li> </ul> <h2 id="research-context--future-directions">Research Context &amp; Future Directions</h2> <p>This Fall 2025 project focused on a user perception study evaluating the efficacy of torso-based role shifting during collaborative tasks. While originally envisioned to connect temporal intent modeling (LSTMs, Transformers) directly with embodied perspective-taking, the intent recognition component has not yet been integrated into the physical robot control loop. Closing this loop—fusing real-time multi-agent intent prediction with embodied communication—remains an active future research objective.</p>