arXiv subjects

Jingyang You

Publications and source records attributed to Jingyang You.

4 recordsLinked to original sources

arXiv2025

Meta-RL with Bayesian Linear Task Models

Deep Bayesian reinforcement learning adapts to unseen tasks by inferring latent transition and reward models, but existing methods typically rely on variational posteriors and evidence lower bounds, introducing approximation error and unstable task representations. We introduce GLiBRL, a deep Bayesian RL framework that combines generalised linear task models with learnable non-linear basis functions. GLiBRL features conjugate Bayesian inference, yielding exact, sequential posterior updates over task parameters and model noise, together with a closed-form marginal likelihood that eliminates variational inference. The update is naturally permutation-invariant, allowing GLiBRL to integrate with both off- and on-policy algorithms. GLiBRL also learns task representation admitting an exact kernel identity, relating distances between task representations to kernel discrepancies over the task contexts. Compared against eight representative or recent meta reinforcement learning methods, GLiBRL achieves the highest aggregate zero-shot test performance on both the MuJoCo locomotion and MetaWorld manipulation benchmarks.

cs.LG↗

arXiv2025

Inspection Planning Primitives with Implicit Models

The aging and increasing complexity of infrastructures make efficient inspection planning more critical in ensuring safety. Thanks to sampling-based motion planning, many inspection planners are fast. However, they often require huge memory. This is particularly true when the structure under inspection is large and complex, consisting of many struts and pillars of various geometry and sizes. Such structures can be represented efficiently using implicit models, such as neural Signed Distance Functions (SDFs). However, most primitive computations used in sampling-based inspection planner have been designed to work efficiently with explicit environment models, which in turn requires the planner to use explicit environment models or performs frequent transformations between implicit and explicit environment models during planning. This paper proposes a set of primitive computations, called Inspection Planning Primitives with Implicit Models (IPIM), that enable sampling-based inspection planners to entirely use neural SDFs representation during planning. Evaluation on three scenarios, including inspection of a complex real-world structure with over 92M triangular mesh faces, indicates that even a rudimentary sampling-based planner with IPIM can generate inspection trajectories of similar quality to those generated by the state-of-the-art planner, while using up to 70x less memory than the state-of-the-art inspection planner.

cs.RO↗

arXiv2022

Emergent Topological Superconductor by Charge Density Wave Transition

Many-body instabilities and topological physics are two attractive topics in condensed matter physics. It is intriguing to explore the interplay between these phenomena in a single quantum material. Here, using the prototypical charge density wave (CDW) material monolayer 1H-NbSe$_2$ as an example, we show how momentum-dependent electron-phonon coupling drives the CDW transition from $3\times3$ to $2\times2$ phase under electron doping. More interestingly, we find the coexistence of superconductivity and nontrivial topology in one of the two $2\times2$ CDW phases, the latter of which is identified by the nonzero Z$_2$ invariant with ideal Dirac cone edge states near the Fermi level. A similar CDW transition-induced topological superconductor has also been confirmed in monolayer 1H-TaSe$_2$. Our findings not only reveal a unique and general method to introduce nontrivial topology by CDW transition, but also provide an ideal platform to modulate different quantum orders by electron doping, thus stimulating experimental interest.

cond-mat.supr-con↗

arXiv2021

T-carbon: experiments, properties, derivatives and potential applications

Carbon is an extremely versatile element and carbon allotropes are very useful in all aspects of life and scientific research. T-carbon is a novel carbon allotrope with many appealing properties. Since the proposal of T-carbon, there has been a lot of intensive studies devoted to its physical, chemical, optical, magnetic, thermoelectrical and topological properties and possible applications in diverse areas in recent years. In this review, we provide a comprehensive review on the advances of the experiments, intriguing properties and various potential applications of T-carbon in energy storage, optoelectronics, thermoelectrics, topological states, etc. As intercalation and doping are effective methods to modify the electronic structure of materials or even to convert into sparkly different new structures or phases, we also discuss different atom doped T-carbon, which exhibit more intriguing properties and lead to promising potential applications in solar cells, photocatalysis, magnetism, superconductivity and so on. In addition, it is interesting to mention that the hydrogenated T-carbon molecules werefound to be possibly related to the physical origin of the UV extinction feature in interstellar medium that has been the half-century long unsolved puzzle. Many novel derivative structures either derived or inspired from T-carbon are included as well. Finally, we give prospects and outlook for future directions of study on T-carbon and related structures.

cond-mat.mtrl-sci↗