arXiv subjects

Xueming Xiao

Publications and source records attributed to Xueming Xiao.

3 recordsLinked to original sources

arXiv2026

RecMorph: Topology-Guided Spatial Recurrence for Generalized Morphology Control

Generalized morphology control requires a single policy to transform information across limbs with different physical roles, coordinate whole-body motion, and remain efficient as body size grows. Existing communication mechanisms address these requirements only partially. We introduce RecMorph, a topology-guided spatial recurrent architecture that uses recurrent sequence computation to jointly perform cross-limb communication and representation transformation. A depth-first traversal converts the kinematic tree into a morphology-derived sequence, along which shared bidirectional transitions progressively transform limb information before action decoding. Residual preservation, RMS normalization, and input-dependent channel modulation stabilize this repeated spatial transformation, yielding linear token complexity at fixed model width and depth. Across five UNIMAL tasks, RecMorph achieves the strongest mean final training performance among the evaluated generalized morphology controllers and the highest measured inference throughput on FT, while generalizing to unseen variations and bodies with up to 30 limbs. We further migrate representative generalized controllers from UNIMAL benchmarks to a four-platform quadruped setting. RecMorph achieves the best macro-averaged performance under nominal and high friction, reduces nominal velocity RMSE by 43.5% relative to specialist MLPs, and one shared policy completes 40 physical Go1/Go2 trials without falls. These results show that topology-guided recurrent transformation provides an effective and efficient communication mechanism for Generalized Morphology Control and remains effective when transferred from procedural bodies to physical robot platforms. Code and experimental resources are publicly available at https://github.com/quanruirao/RecMorph.

cs.RO↗

arXiv2025

GCNT: Graph-Based Transformer Policies for Morphology-Agnostic Reinforcement Learning

Training a universal controller for robots with different morphologies is a promising research trend, since it can significantly enhance the robustness and resilience of the robotic system. However, diverse morphologies can yield different dimensions of state space and action space, making it difficult to comply with traditional policy networks. Existing methods address this issue by modularizing the robot configuration, while do not adequately extract and utilize the overall morphological information, which has been proven crucial for training a universal controller. To this end, we propose GCNT, a morphology-agnostic policy network based on improved Graph Convolutional Network (GCN) and Transformer. It exploits the fact that GCN and Transformer can handle arbitrary number of modules to achieve compatibility with diverse morphologies. Our key insight is that the GCN is able to efficiently extract morphology information of robots, while Transformer ensures that it is fully utilized by allowing each node of the robot to communicate this information directly. Experimental results show that our method can generate resilient locomotion behaviors for robots with different configurations, including zero-shot generalization to robot morphologies not seen during training. In particular, GCNT achieved the best performance on 8 tasks in the 2 standard benchmarks.

cs.RO↗

arXiv2024

Passive None-line-of-sight imaging with arbitrary scene condition and detection pattern in small amount of prior data

Passive Non-Line-of-Sight (NLOS) imaging requires to reconstruct objects which cannot be seen in line without using external controllable light sources. It can be widely applied in areas like counter-terrorism, urban-Warfare, autonomous-driving and robot-vision. Existing methods for passive NLOS typically required extensive prior information and significant computational resources to establish light transport matrices or train neural networks. These constraints pose significant challenges for transitioning models to different NLOS scenarios. Thus, the pressing issue in passive NLOS imaging currently lies in whether it is possible to estimate the light transport matrices which corresponding to relay surfaces and scenes, as well as the specific distribution of targets, with a small amount of prior knowledge. In this work, we hypothesized a high-dimensional manifold and mathematically proved its existence. Within this high-dimensional manifold, the structural information of obscured targets is minimally disrupted. Therefore, we proposed a universal framework named High-Dimensional Projection Selection (HDPS) which can establish this high-dimensional manifold and output its projection onto corresponding surfaces on low-dimensional. HDPS can be applied to most mature network architectures and estimate the distribution of target and light spot obtained by camera with only minimal prior data. Certainly, with the help of the estimated information, it can establish a high-dimensional manifold consisting of target and input. As demonstrated in experiment, our framework, even when applied to the most basic network structures, can achieve higher accuracy results with significantly smaller amounts of prior data. Thereby, our approach enables passive NLOS scenarios to reconstruct target by limited prior data and computational resources.

physics.optics↗