Tsinghua Team Shows Drosophila Connectome Enables Adaptable Embodied Control

A research team from Tsinghua University demonstrates that a fixed neural network core derived from the adult Drosophila connectome enables adaptable embodied control in simulated agents. The study shows that while performance is comparable to multi-layer perceptrons (MLPs) on flat terrain, the connectome-based controller significantly outperforms MLPs and other baselines when facing changes in environment (obstacles), task requirements (turning, speed variation), or body morphology (two-legged to six-legged robots). The research reveals that mechanical sensation and ascending neuron pathways are spontaneously recruited to handle these variations, suggesting that evolutionarily optimized neural structures provide reusable priors for robust motion control.