Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study develops a mathematical framework for expressing perceptual uncertainty in robotic motion, utilizing Laban Movement Analysis to categorize robot behaviors into a Commitment-Vigilance state space. By parameterizing five motion primitives with eleven kinematic and geometric descriptors, the research identifies how these factors influence human perception of robotic expressiveness. The findings demonstrate that participants can reliably interpret the intended emotional states of robots, highlighting the potential for improved non-verbal communication in human-robot collaboration.
Robots can convey complex emotional states through motion, with specific kinematic features significantly enhancing human interpretability.
Robots operating in human-robot collaboration must communicate not only their intended actions but also uncertainty arising from incomplete or ambiguous perception. This work introduces a mathematical framework for expressing perceptual uncertainty through robotic manipulator motion. Drawing on Laban Movement Analysis, robot behavior is organized in a Commitment-Vigilance state space that maps uncertainty-related states - confidence, curiosity, hesitance, fear, and inactivity - to distinct Laban Effort signatures. Five motion primitives - approach, pause, retreat, exploration, and oscillation - are then parameterized using eleven kinematic and geometric descriptors, including acceleration, pause and retreat characteristics, gaze angles, tilt, and shiver amplitude. A video-based human-subject study evaluated recognition of four expressive trajectories and the influence of individual descriptors on perceived intensity. Participants reliably identified the intended behavioral states, while several descriptors significantly modulated expressiveness. The results establish a perceptually grounded basis for encoding robot uncertainty in motion and support future autonomous trajectory generation using parametric movement representations for collaborative tasks in shared environments. Code, videos, questionnaire and appendices are available at"https://bit.ly/github-aou".