Evolving Physical Instinct for Morphology and Control Co-Adaption

The capability of a robot to perform tasks depends not only on precise motion control, but also on a well-suited body morphology. Adapting both morphology and control of robots to improve their task performance has been a widely studied and long-standing issue. While the bio-inspired bilevel optimization framework has gained popularity in recent years, it suffers from high computation complexity due to the time-consuming and inefficient learning process for each morphology. In fact, in nature, besides the adaptive morphology and the intelligent brain, animals also possess an important gift, which is physical instinct. These instincts allow animals to respond quickly to their surroundings in the neonatal period, facilitating skills acquisition. Inspired by this, we propose an evolvable instinct controller to enhance the morphology-control co-adaption. The instinct controller suggests rough motion inclinations, which require minimal domain knowledge and entail less sophisticated design. Its purpose is to assist the main controller in learning fine-grained and robust control efficiently. We implemented this idea in the context of legged locomotion and designed the instinct controller using phase-based FSMs. We propose the instinct-based co-adaption algorithm and construct GPU parallel simulation experiments on different morphology prototypes. The results indicate that combining the co-adaption process with instinct evolution leads to the development of superior morphologies and robust controllers compared with the conventional co-adaption approach, with minimal additional time cost.