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An Intelligent Framework for Reconstructing Demonstration-Based Trajectories in Robotic Programming Systems
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Keywords
Robot Programming by Demonstration
Trajectory Reconstruction
Intelligent Framework
Spline Interpolation
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Abstract
Robot Programming by Demonstration (PbD) has emerged as a transformative paradigm for enabling intuitive human–robot interaction, where robots acquire skills through observation rather than explicit coding. However, reconstructing accurate and generalized trajectories from demonstrations remains a critical challenge due to variability in human motion, noise in sensing systems, and limitations in modeling complex task constraints. This paper proposes an intelligent framework for reconstructing demonstration-based trajectories in robotic programming systems, integrating probabilistic learning, spline-based trajectory optimization, and sensor-driven interaction models. The framework systematically processes raw demonstration data, extracts relevant motion features, and reconstructs smooth and adaptable trajectories using constrained interpolation techniques. Furthermore, it incorporates augmented interaction interfaces to enhance data acquisition fidelity and system responsiveness. The proposed approach is evaluated through analytical modeling and comparative assessment with existing techniques, demonstrating improved robustness, adaptability, and computational efficiency. The findings highlight the significance of combining statistical learning with geometric modeling for advancing PbD systems in industrial and service robotics.
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