Imitation LearningExpert
Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch
This arXiv preprint introduces EF-GAIfO, a feasibility-aware imitation-from-observation method that learns robot policies from state-only human demonstrations by scoring each demonstrated transition against the robot's own accumulated experience. On a Unitree Go2 quadruped, policies learned with EF-GAIfO reached 70–80% real-world object-grasping success versus 10–30% for WGAIfO and GAIfO baselines. The approach matters because it removes the need for action-labeled demonstrations or explicit robot dynamics models when learning from human motion that the robot may not be able to reproduce.