Insect Respiratory Biomechanics and Bioinspiration
Mechanisms of insect respiration
Insect respiration is characterized by the rapid transport of respiratory gases within the organism and efficient exchange with the external environment. The unique respiratory system of insects comprises a network of tracheal tubes that directly supply oxygen to the cells throughout the body of an insect, eliminating the need for blood as an intermediate oxygen carrier. The remarkable diversity of insects and
their exceptionally high aerobic scope, possibly the highest in the
animal kingdom, demonstrate the success of their respiratory strategy. Microfluidic technology, particularly in the domain of gas microfluidics, also stands to benefit from emulating the mechanical proficiency demonstrated by insects in manipulating fluids at the microscale. Despite this significance, current understanding of the fundamental principles underlying insect respiration is incomplete. This Review presents an overview of insect respiratory physics and identifies promising areas for future investigations.
Khan, S., Staples, A.E. Mechanisms of insect respiration. Nat Rev Phys 7, 135–148 (2025). https://doi.org/10.1038/s42254-025-00811-x
Frequency-specific, valveless flow control in insect-mimetic microfluidic devices
Inexpensive, portable lab-on-a-chip devices would revolutionize fields like environmental monitoring and global health, but current microfluidic chips are tethered to extensive off-chip hardware. Insects, however, are self-contained and expertly manipulate fluids at the microscale using largely unexplored methods. We fabricated a series of microfluidic devices that mimic key features of insect respiratory kinematics observed by synchrotron-radiation imaging, including the collapse of portions of multiple respiratory tracts in response to a single fluctuating pressure signal. In one single-channel device, the flow rate and direction could be controlled by the actuation frequency alone, without the use of internal valves. Additionally, we fabricated multichannel chips whose individual channels responded selectively (on with a variable, frequency-dependent flow rate, or off) to a single, global actuation frequency. Our results demonstrate that insect-mimetic designs have the potential to drastically reduce the actuation overhead for microfluidic chips, and that insect respiratory systems may share features with impedance mismatch pumps.
Chatterjee, K. et al. Frequency-specific, valveless flow control in insect-mimetic microfluidic devices. Bioinspir Biomim 16, 036004 (2021). https://doi.org/10.1088/1748-3190/abe4bc
Insect-inspired modular architectures as inductive biases for reinforcement
learning
Most reinforcement-learning (RL) controllers used in continuous control are architecturally centralized: observations are compressed into a single latent state from which both value estimates and actions are produced. Biological control systems are often organized differently. Insects, in particular, coordinate navigation, heading stabilization, memory, and context-dependent action selection through distributed circuits rather than a single monolithic controller. Motivated by his contrast, we study an RL policy architecture that decomposes control into interacting modules for sensory encoding, heading representation, sparse associative memory, recurrent command generation, and local motor control, with a learned arbitration mechanism that allocates motor authority across modules. The model is evaluated on a two-dimensional navigation task that require simultaneous food seeking, obstacle avoidance, and predator escape. In a six-seed predator-navigation experiment trained with Proximal Policy Optimization (PPO) for 75 updates, the modular policy achieves the strongest final mean performance among the tested controllers, with final episodic return −2798.8 ± 964.4 versus −3778.0 ± 628.1 for a centralized gated recurrent unit (GRU) and −4727.5 ± 772.5 for a centralized multilayer perceptron (MLP). The modular policy also attains the lowest final value loss and stable PPO optimization statistics while driving module-assignment entropy to 0.0457 ± 0.0244, indicating highly selective control allocation. These results suggest that distributed control can serve as a useful inductive bias for RL problems involving dynamically competing behavioral objectives
Staples, A. E. Insect-inspired modular architectures as inductive biases for reinforcement learning. arXiv:2604.22081 [cs.LG] (2026). https://doi.org/10.48550/arXiv.2604.22081
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