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Pengxi Gu

Publications and source records attributed to Pengxi Gu.

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Population-Scale Advancing Interface Modeling Reveals How Bacterial Swarms Encode Future Spatial Architecture

Motile bacteria shape microbial function by occupying space, yet how collective motion becomes population-scale architecture remains poorly resolved. Bacterial swarming is not merely surface motion, but a process by which motile populations commit to future macroscopic form. Here, in Enterobacter sp. SM3, a gut-associated swarmer linked to mucosal repair, we treat the advancing colony--environment interface as a morphodynamic state through which local motility becomes spatial order. We built SwarmEvo across thermal, hydration, and substrate-mechanical conditions and developed Morpher to resolve and propagate interface states. Counterintuitively, within the permissive assay range, condition labels only weakly separated future trajectories, whereas colony-specific interface geometry constrained later expansion, indicating that swarm fate is written into the interface rather than prescribed by condition identity. Boundary fidelity was decisive: a 0.67 percentage-point segmentation gap expanded into a 2.4--3.1 IoU-point forecasting loss. Preserving front displacement, protrusion continuity, and branch memory, Morpher predicted late-stage expansion with 95.42% mIoU, 10.61 px HD$_{95}$, and 3.93 px ASSD. These results identify the advancing interface as a state-bearing layer through which motility and environmental constraint are converted into future spatial form, enabling disease-relevant microbial organization to be read before endpoint architecture emerges.

cond-mat.soft

Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of `intelligent design under constraints' - hereafter referred to as constraint-driven design intelligence - in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4-bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85 meters per second on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.

cs.RO