A Python-Based Framework for Learning-from-Demonstration in Robotic Object Sorting: Comparative Evaluation of Lightweight Classifiers

This paper presents a Python-based v3.12 framework for robotic object sorting in a virtual workcell, combining learning-from-demonstration with a comparative evaluation of classical machine learning classifiers. A user provides a minimal demonstration (e.g., one cube and one cylinder placed into two bins) from which a dynamic type-to-bin rule is inferred. In this study, learning-from-demonstration is implemented at the level of rule acquisition from minimal task examples rather than at the level of trajectory imitation or low-level motion teaching. This rule is used to relabel a larger dataset of pre-generated object positions, enabling training with a selectable number of file-based samples (2–1600) optionally augmented with manual samples. Five classifiers—decision tree, k-nearest neighbors, logistic regression, naive Bayes, and linear SVM—were trained and then used to drive autonomous pick-and-place execution while logging replication time and correctness (correct/incorrect moves and accuracy). Because the task reaches accuracy saturation under a deterministic rule, an additional offline inference benchmark was included to compare prediction throughput using 10,000 probes with repeated timing (median over 50 runs or mean ± standard deviation over 30 runs). To complement this nominal evaluation, the framework also included a perturbation-aware robustness protocol based on controlled positional perturbation, systematic bias, controlled shape corruption, repeated perturbation voting, and stability-aware scoring. This additional layer makes it possible to examine classifier behavior under controlled uncertainty, especially in reduced-data settings, without changing the compact simulator-based nature of the workflow. Results indicate identical sorting accuracy across models, while inference-time differences remain measurable, highlighting deployment-oriented trade-offs and confirming that end-to-end cycle time is dominated by robot motion rather than model computation.