Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

Alperen Kenan, Paul Bremner, Manuel Giuliani|August 6, 2026arXiv

Key Takeaway

By incorporating force, timing, and multi-segment trajectory handling into standard learning-from-demonstration techniques, robots can generate handwriting that humans perceive as significantly more natural and human-like.

Summary

This paper presents a framework for teaching robots to write letters by learning from human handwriting demonstrations. The approach combines force and timing data with trajectory learning to generate robot motions that feel natural to humans. A user study confirmed the generated handwriting was perceived as 71.5% human-like, with the dataset released openly for future research.

training agents applications

Key Terms

learning-from-demonstration gaussian-mixture-model gaussian-mixture-regression trajectory-learning human-robot-interaction