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Researcher profile

Hao Luan

Hao Luan contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputer Visioneess.SYHardware ArchitectureMachine LearningRoboticsSystems and Control

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Published work

4 published item(s)

preprint2026arXiv

Inference-Time Attribute Distribution Alignment for Unconditional Diffusion

Inference-time controllable generation is essential for real-world applications of unconditional diffusion models. However, most existing techniques focus on individual samples, struggling in applications that require the sample population to follow specific attribute distributions (e.g., demographic balance or semantic proportions). We formalize this setting as the inference-time attribute distributional alignment problem for pretrained unconditional diffusion models. To address this, we cast inference-time attribute distributional alignment as an optimal control problem over the reverse diffusion process, viewing the process as the rollout of a dynamical system and augmenting it with additive, time-dependent perturbations as control. We solve for the perturbations using an optimal-control-based algorithm to optimize a differentiable distribution-matching objective while penalizing control effort to preserve data fidelity. Experiment results in image generation demonstrate that our proposed plug-and-play approach can better align attribute distributions to diverse and flexible test-time targets compared to baselines, without retraining or finetuning the pretrained diffusion model.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

vSTMD: Visual Motion Detection for Extremely Tiny Target at Various Velocities

Visual motion detection for extremely tiny (ET-) targets is challenging, due to their category-independent nature and the scarcity of visual cues, which often incapacitate mainstream feature-based models. Natural architectures with rich interpretability offer a promising alternative, where STMD architectures derived from insect visual STMD (Small Target Motion Detector) pathways have demonstrated their effectiveness. However, previous STMD models are constrained to a narrow velocity range, hindering their efficacy in real-world scenarios where targets exhibit diverse and unstable dynamics. To address this limitation, we present vSTMD, a learning-free model for motion detection of ET-targets at various velocities. Our key innovations include: (1) a cross-Inhibition Dynamic Potential (cIDP) that serves as a self-adaptive mechanism efficiently capturing motion cues across a wide velocity spectrum, and (2) the first Collaborative Directional Gradient Calculation (CDGC) strategy, which enhances orienting accuracy and robustness while reducing computational overhead to one-eighth of previously isolated strategies. Evaluated on the real-world dataset RIST, the proposed vSTMD and its feedback-facilitated variant vSTMD-F achieve relative $F_{1}$ gains of $30\%$ and $58\%$ over state-of-the-art (SOTA) STMD approaches, respectively. Furthermore, both models demonstrate competitive orientation estimation performance compared to SOTA deep learning-driven methods. Experiments also reveal the superiority of the natural architecture for ET-object motion detection - vSTMD is $60\times$ faster than contemporary data-driven methods, making it highly suitable for real-time applications in dynamic scenarios and complex backgrounds. Code is available at https://github.com/MingshuoXu/vSTMD.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Many-ported and Shared Memory Architecture for High-Performance ADAS SoCs

Increasing investment in computing technologies and the advancements in silicon technology has fueled rapid growth in advanced driver assistance systems (ADAS) and corresponding SoC developments. An ADAS SoC represents a heterogeneous architecture that consists of CPUs, GPUs and artificial intelligence (AI) accelerators. In order to guarantee its safety and reliability, it must process massive amount of raw data collected from multiple redundant sources such as high-definition video cameras, Radars, and Lidars to recognize objects correctly and to make the right decisions promptly. A domain specific memory architecture is essential to achieve the above goals. We present a shared memory architecture that enables high data throughput among multiple parallel accesses native to the ADAS applications. It also provides deterministic access latency with proper isolation under the stringent real-time QoS constraints. A prototype is built and analyzed. The results validate that the proposed architecture provides close to 100\% throughput for both read and write accesses generated simultaneously by many accessing masters with full injection rate. It can also provide consistent QoS to the domain specific payloads while enabling the scalability and modularity of the design.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robotic Autonomous Trolley Collection with Progressive Perception and Nonlinear Model Predictive Control

Autonomous mobile manipulation robots that can collect trolleys are widely used to liberate human resources and fight epidemics. Most prior robotic trolley collection solutions only detect trolleys with 2D poses or are merely based on specific marks and lack the formal design of planning algorithms. In this paper, we present a novel mobile manipulation system with applications in luggage trolley collection. The proposed system integrates a compact hardware design and a progressive perception and planning framework, enabling the system to efficiently and robustly collect trolleys in dynamic and complex environments. For the perception, we first develop a 3D trolley detection method that combines object detection and keypoint estimation. Then, a docking process in a short distance is achieved with an accurate point cloud plane detection method and a novel manipulator design. On the planning side, we formulate the robot's motion planning under a nonlinear model predictive control framework with control barrier functions to improve obstacle avoidance capabilities while maintaining the target in the sensors' field of view at close distances. We demonstrate our design and framework by deploying the system on actual trolley collection tasks, and their effectiveness and robustness are experimentally validated.

0 citations0 reviewsNo code linkedOpen access