Learning from Yangtze Alligators: A framework for agile and ecologically interactive spine-legged robots

A central challenge in bio-robotics is to create machines that can integrate into and illuminate natural ecosystems. The Chinese Yangtze Alligator —a critically endangered species exhibiting exceptionally agile spine-leg coordination honed by its terrestrial-aquatic transition—offers a unique model to address this challenge. Yet, existing alligators-like robots fail to capture such biological fidelity due to insufficient actuation, simplified mechanics, and the absence of adaptive control policies. Here, we introduce the Spine-Legged Adversarial Imitation and Reinforcement Learning (SLAIR) framework, which for the first time leverages deep reinforcement learning to master this coordination. By retargeting biological motion data from Yangtze alligators and integrating impedance control to produce natural compliance, our controller achieves adaptive spine-leg coordination in a custom 24-DOFs robot. A variational autoencoder (VAE) generalizes across terrain, while a dual-critic architecture robustly fuses imitation and task rewards. This enables agile locomotion (0.32 m/s, 360° turns in 3.5 s) with a 46.7% reduction in cost of transport. Crucially, the robot’s biomimetic fidelity was validated in the field, where it elicited natural curiosity and approach behavior from wild Yangtze alligators—demonstrating its potential as a transformative tool for conservation biology.