Huailiang Ma

AutoTrialGen: Automated Data Generation From Few Human Demonstrations via Trajectory Annotation and Simulation Trials

While imitation learning is a powerful paradigm for teaching robots complex manipulation skills, its effectiveness is often bottlenecked by the need for large-scale, human-collected datasets. This paper presents AutoTrialGen, an automated framework designed to generate large and diverse datasets of