arXiv subjects

Zhelin Zhang

Publications and source records attributed to Zhelin Zhang.

3 recordsLinked to original sources

arXiv2026

Phlystron -- A photonic terahertz amplifier

High-energy (mJ) and high-peak-power (MW) multicycle terahertz (THz) pulses are essential for nonlinear THz spectroscopy and compact accelerator technologies, yet their generation by nonlinear optical frequency conversion remains inefficient and imposes severe demands on femtosecond driving lasers. Amplifying existing THz pulses offers an appealing alternative, but no power-scalable amplifier has been realized in the sub-THz regime. Here, we demonstrate an all-optical THz amplifier operating at 0.35 THz based on the modulation of nanosecond laser pulses by a weak THz field in periodically poled lithium niobate (PPLN). The THz-induced phase modulation is converted into an amplitude modulation using controlled group delay dispersion, forming a tailored pulse train that can efficiently drive high-energy THz generation in a second crystal, thereby amplifying the THz seed. By analogy to electronic klystrons, we term this device the Phlystron, in which the electron beam carrying the power is replaced by a photon beam. In this proof-of-concept experiment, a 3.3-fold increase in THz energy is achieved with commercial crystals. Scaling analysis indicates the potential for higher gain when using large-aperture PPLN devices and multi-stage amplification. The Phlystron thus provides a scalable route to powerful multicycle THz sources driven by readily available narrowband lasers.

physics.optics↗

arXiv2025

PegasusFlow: Parallel Rolling-Denoising Score Sampling for Robot Diffusion Planner Flow Matching

Diffusion models offer powerful generative capabilities for robot trajectory planning, yet their practical deployment on robots is hindered by a critical bottleneck: a reliance on imitation learning from expert demonstrations. This paradigm is often impractical for specialized robots where data is scarce and creates an inefficient, theoretically suboptimal training pipeline. To overcome this, we introduce PegasusFlow, a hierarchical rolling-denoising framework that enables direct and parallel sampling of trajectory score gradients from environmental interaction, completely bypassing the need for expert data. Our core innovation is a novel sampling algorithm, Weighted Basis Function Optimization (WBFO), which leverages spline basis representations to achieve superior sample efficiency and faster convergence compared to traditional methods like MPPI. The framework is embedded within a scalable, asynchronous parallel simulation architecture that supports massively parallel rollouts for efficient data collection. Extensive experiments on trajectory optimization and robotic navigation tasks demonstrate that our approach, particularly Action-Value WBFO (AVWBFO) combined with a reinforcement learning warm-start, significantly outperforms baselines. In a challenging barrier-crossing task, our method achieved a 100% success rate and was 18% faster than the next-best method, validating its effectiveness for complex terrain locomotion planning. https://masteryip.github.io/pegasusflow.github.io/

cs.RO↗

arXiv2025

Whole-Body Constrained Learning for Legged Locomotion via Hierarchical Optimization

Reinforcement learning (RL) has demonstrated impressive performance in legged locomotion over various challenging environments. However, due to the sim-to-real gap and lack of explainability, unconstrained RL policies deployed in the real world still suffer from inevitable safety issues, such as joint collisions, excessive torque, or foot slippage in low-friction environments. These problems limit its usage in missions with strict safety requirements, such as planetary exploration, nuclear facility inspection, and deep-sea operations. In this paper, we design a hierarchical optimization-based whole-body follower, which integrates both hard and soft constraints into RL framework to make the robot move with better safety guarantees. Leveraging the advantages of model-based control, our approach allows for the definition of various types of hard and soft constraints during training or deployment, which allows for policy fine-tuning and mitigates the challenges of sim-to-real transfer. Meanwhile, it preserves the robustness of RL when dealing with locomotion in complex unstructured environments. The trained policy with introduced constraints was deployed in a hexapod robot and tested in various outdoor environments, including snow-covered slopes and stairs, demonstrating the great traversability and safety of our approach.

cs.RO↗