Experience-Based Feasibility-Aware Generative Adversarial Imitation from Observation under Embodiment Mismatch
EF-GAIfO enhances imitation learning by checking if demonstrated robot movements are actually possible based on what the robot has experienced. It estimates motion feasibility using a reconstruction model trained on past experiences, assigning weights to demonstrations. This allows the policy to ignore infeasible actions, leading to better performance in real-world tasks despite mismatches.