Insect locomotion exhibits remarkable adaptability and flexibility despite the limited scale of its nervous system. However, the underlying principles that govern leg coordination remain difficult to extract and model. This paper presents an adversarial inverse reinforcement learning approach to infer transferable locomotor principles from limited insect behavioral data, enabling robust robot locomotion.
