Neural Circuit Architectural Priors for Quadruped Locomotion

Abstract: Learning-based approaches to quadruped locomotion commonly adopt generic policy architectures like fully connected MLPs. As such architectures contain few inductive biases, it is common in practice to incorporate priors in the form of rewards, training curricula, imitation data, or trajectory generators. In nature, animals are born with priors in the form of their nervous system's architecture, which has been shaped by evolution to confer innate ability and efficient learning. For instance, a horse can walk within hours of birth and can quickly improve with practice. Such architectural priors can also be useful in ANN architectures for AI. In this work, we explore the advantages of a biologically inspired ANN architecture for quadruped locomotion based on neural circuits in the limbs and spinal cord of mammals. Our architecture achieves good initial performance and comparable final performance to MLPs, while using less data and orders of magnitude fewer parameters. Our architecture also exhibits better generalization to task variations, even admitting deployment on a physical robot without standard sim-to-real methods. This work shows that neural circuits can provide valuable architectural priors for locomotion and encourages future work in other sensorimotor skills.

• The paper introduces a neural circuit model that embeds rhythmic and patterned priors for efficient quadruped locomotion.
• Its architecture achieves comparable performance to MLPs using only 92 parameters, significantly reducing training data and compute requirements.
• The model generalizes well to unseen terrain and real-world scenarios, demonstrating robust sim-to-real transfer without standard randomization techniques.

Overview of "Neural Circuit Architectural Priors for Quadruped Locomotion"

This paper presents a biologically inspired approach to controlling quadruped locomotion through the use of Neural Circuit Architectural Priors (NCAP). The authors propose a novel artificial neural network (ANN) architecture modeled on the neural circuits found in mammalian limbs and spinal cords. Their architecture aims to improve initial performance and generalization in robotic quadrupedal locomotion while using significantly fewer parameters than traditional models.

Main Contributions

The paper makes several important contributions to the field of robotics and AI. Notably, it introduces the first neural circuit model specifically designed for quadrupedal robots. The architecture leverages insights from neuroscience, particularly the organization of rhythm generation (RG) and pattern formation (PF) circuits observed in mammals. This biologically inspired structure helps achieve efficient and adaptive locomotion by providing the ANN with built-in inductive biases that mimic natural processes.

Performance Evaluation

The performance evaluation of NCAP is robust. Compared to the widely used multilayer perceptrons (MLPs), NCAP demonstrates comparable asymptotic performance while requiring significantly less training data and fewer parameters. Remarkably, the NCAP architecture uses orders of magnitude fewer parameters—only 92 during testing—compared to over 79,000 in a typical MLP architecture. This parameter efficiency makes it especially suitable for deployment in resource-constrained environments such as mobile robotics.

Generalization Capabilities

A critical finding of the study is the superior generalization capabilities exhibited by the NCAP architecture. The authors demonstrate that NCAP successfully adapts to unseen variations in terrain and body configurations. Furthermore, it is notably effective in sim-to-real transfer, showing marked robustness when deployed on a physical quadruped robot without the need for standard domain randomization techniques often necessary in sim-to-real applications. This indicates the architecture's innate ability to handle noise and perturbations, potentially translating to more robust real-world applications.

Theoretical and Practical Implications

The integration of architectural priors based on neural circuits provides a compelling direction for AI research, merging the gap between biological inspiration and artificial implementations. This work underscores the potential of incorporating biologically inspired neural architectures to enhance data efficiency, performance stability, and generalization in machine learning models, particularly in complex sensorimotor skills such as locomotion.

Future Directions

While the results are promising, the study acknowledges areas for further research. Future work could explore dynamic speed regulation and transition between gaits, incorporating more complex postural adjustments and turning maneuvers. There's also potential in extending the approach to other domains of sensorimotor control beyond locomotion. Additionally, combining these architectural priors with other priors, such as reward shaping or imitation learning, could further improve performance.

Conclusion

The paper provides compelling evidence for the utility of biologically inspired ANN architectures in enhancing quadruped locomotion. By embedding neural circuit-inspired inductive biases, the NCAP architecture excels in parameter efficiency and adaptability, setting a promising precedent for exploring similar approaches in broader application areas within AI and robotics.