Drosophila brain inspires efficient communication for robot limbs
FlyCNS: Connectome-Grounded Information Organization for Communication-Constrained Embodied Control
Robotics
Summary
Robots have sensors and motors spread across their bodies, but usually their control systems process everything in one place, which can be inefficient. This paper by the authors looks at how to organize information in a way that only important messages get sent between parts of the robot, saving communication resources. They take inspiration from how the fruit fly’s nervous system is wired, using local processing in each limb and specialized pathways for sending information long distances. Their approach helps a walking robot keep good performance even when communication is very limited.
What this means in practice
- •For robotics engineers: Optimize communication usage in multi-limbed robots for efficient movement control under limited bandwidth.
- •For autonomous vehicle designers: Design distributed control systems that maintain stable navigation while minimizing data transmission between subsystems.
Authors
Jinchang Zhang, Jiakai Lin, Guoyu Lu
Abstract
Robotic bodies are inherently distributed in sensing and actuation, yet learning-based control still commonly relies on centralized information processing. This work studies the problem of information organization in communication-constrained embodied control: which computations should remain local, and which information is worth transmitting for whole-body coordination. We propose FlyCNS, an embodied information-organization framework inspired by the Drosophila brain--nerve-cord connectome. FlyCNS preserves local sensorimotor computation within each limb and enables selective long-range communication through separate ascending and descending routing pathways. From a real connectome, FlyCNS extracts the directional structural complexity of these two pathway types and uses it as a weak prior over communication allocation, while message content, transmission timing, and locomotion policies remain task-adaptive and are learned through reinforcement learning. In Unitree Go1 simulation, FlyCNS exhibits more graceful performance degradation as the communication budget is tightened. Under the most restrictive setting, it uses only about 21--22\% of the communication of the full-communication reference, while still maintaining a tracking score of approximately 0.882 under both command protocols, with a gap of no more than 6.1\% from the full-communication reference. These results indicate that real neural connectomes can inform not only the structural design of control networks, but also provide transferable inductive biases for information organization across embodiments, guiding robots in balancing local computation and long-range coordination under limited communication resources.