Beyond the Teacher: Leveraging Mixed-Skill Demonstrations for Robust Imitation Learning

Abstract

Achieving expert-like robotic task execution in dynamic environments typically requires extensive, high-quality expert demonstrations, a significant bottleneck for real-world deployment. We present a novel learning framework that overcomes this data dependency, enabling robots to perform complex periodic tasks with expert-like proficiency, even when learning from naive demonstrations. Our two-stage pipeline first selects a representative demonstration based on user-defined information-aware task intention scores. This single best demo is then used to extract a canonical motion shape via Periodic Dynamic Movement Primitives (DMPs). Finally, a Long Short-Term Memory (LSTM) network refines the entire set of demonstrations,leveraging a multi-objective score that combines the canonical shape with mutual information and other task quality metrics. The proposed approach is demonstrated on a Franka Research 3 robot performing phasic tasks across three contrasting domains: wiping in human assistive services, weaving in the textile industry, and pick-and-place operations for warehouse automation. Visit project page at: https://focaslab.github.io/beyondtheteacher.