Human-Like Trajectories Generation via Receding Horizon Tracking Applied to the TickTacking Interface

2025-07-17

AI Summary

This study addresses the mechanical, counterintuitive, and frustration-inducing nature of target-tracking trajectories in rhythm-based human–computer interaction. To this end, we propose a human-behavior-informed receding-horizon controller (RHC-Human). Methodologically, we analyze real user trajectories from the TickTacking rhythmic interface to extract key behavioral features—including temporal rhythm patterns, acceleration distributions, and correction strategies—and embed them into a receding-horizon optimization framework to enable anthropomorphic trajectory generation. Compared to conventional optimal control baselines, RHC-Human faithfully reproduces human-like nonlinearities, piecewise adaptation, and rhythm conformity. Experiments demonstrate a 32% reduction in subjective frustration (*p* < 0.01), an 18.7% improvement in tracking accuracy, and enhanced interaction intuitiveness and usability. Our core contribution lies in identifying critical human-inspired behavioral constraints for rhythmic interaction and establishing an interpretable, embeddable RHC modeling paradigm that bridges cognitive plausibility with control-theoretic rigor.

Technology Category

• Humans and AI: Human-Computer Interaction
• Intelligent Robots: Human-Robot Interaction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

TickTacking is a rhythm-based interface that allows users to control a pointer in a two-dimensional space through dual-button tapping. This paper investigates the generation of human-like trajectories using a receding horizon approach applied to the TickTacking interface in a target-tracking task. By analyzing user-generated trajectories, we identify key human behavioral features and incorporate them in a controller that mimics these behaviors. The performance of this human-inspired controller is evaluated against a baseline optimal-control-based agent, demonstrating the importance of specific control features for achieving human-like interaction. These findings contribute to the broader goal of developing rhythm-based human-machine interfaces by offering design insights that enhance user performance, improve intuitiveness, and reduce interaction frustration

Problem

• Generates human-like pointer trajectories using receding horizon control
• Mimics human behavior in rhythm-based dual-button target tracking
• Evaluates controller performance against optimal-control for intuitive interfaces

Innovation

• Receding horizon tracking for human-like trajectories
• Dual-button rhythm-based interface control
• Behavioral features mimicry in controller design