From insect behavior to transferable robot locomotion: inferring embodied locomotor principles from limited data via adversarial inverse reinforcement learning
A data-driven framework based on adversarial inverse reinforcement learning is proposed, which directly learns continuous locomotion control policies from stick insect walking data and infers latent reward structures and control strategies from biological behavioral demonstrations, with potential applications to bio-in...