arXiv subjects

Hang Ma

Publications and source records attributed to Hang Ma.

At least 19 recordsLinked to original sources

arXiv2026

Unified Branch-and-Bound Search for the Steiner Traveling Salesman Problem on Graphs of Convex Sets

We formalize the Steiner Traveling Salesman Problem (Steiner-TSP) on Graphs of Convex Sets (GCS), which seeks a minimum-cost closed trajectory through required convex sets while allowing optional transit vertices and revisits. To explore the resulting infinite solution space, we propose a unified branch-and-bound search over rooted walk prefixes. Additive lower-bound-graph costs bound committed prefixes, while a cut-separated connected-flow relaxation lower-bounds the residual cost of visiting every remaining target and returning to the root. Under a uniform positive-cost assumption, best-first traversal terminates after finitely many expansions on every feasible instance without an initial incumbent, whereas depth-first traversal does so once a finite incumbent is available. For a user-specified factor $\epsilon\geq1$, a global lower bound certifies that either strategy's incumbent cost is at most $\epsilon$ times the global optimum. We further demonstrate joint sensing-mode, visitation-order, and continuous-trajectory selection for a mobile-manipulator inspection task, including action precedences expressed in linear temporal logic over finite traces (LTL$_f$). Both traversal strategies find feasible solutions on all benchmark instances within 30s with mean certified optimality gaps of 28.1% and 29.7%, respectively, whereas two recent baselines succeed on only about half of the instances

cs.AI↗

arXiv2026

Goal Staying Makes Sum-of-Costs Anonymous Multi-Agent Path Finding NP-Hard

Anonymous Multi-Agent Path Finding (AMAPF) admits polynomial-time network-flow algorithms for several objectives, including makespan, total distance, and sum-of-costs (SoC) when agents disappear upon reaching goals. We show that standard goal-staying AMAPF is fundamentally different. We first formulate SoC minimization by augmenting the standard time-expanded flow model with goal-settlement constraints and show that the resulting linear programming relaxation is non-integral. We then prove that minimizing SoC in goal-staying AMAPF is NP-hard via a reduction from 3-SAT. Together with the polynomial-time result for the disappearing variant, this establishes a sharp complexity boundary determined by whether completed agents remain at their goals.

cs.MA↗

arXiv2026

Polaron-mediated metal-insulator transition and proton conduction in hydrogenated nickelate perovskites

Nickel-based perovskites, owing to their spontaneous hydrogen uptake and the dramatic increase in resistivity upon hydrogenation, have emerged as promising candidates for proton-conducting fuel cell electrolytes. However, the mechanism of the hydrogen-induced metal-insulator transition (MIT) in rare-earth nickelates remains under debate, particularly regarding whether the doped electrons occupy Ni e$_g$ states or O 2p ligand hole states. Here, we reveal a comprehensive MIT mechanism using first-principles calculations on NdNiO$_3$: the electrons introduced by hydrogen doping occupy the O 2p ligand hole states of the Ni-O hybridized d$_8$L configuration, promoting electron-polaron formation. The resulting electron polarons, together with proton polarons, weaken the Ni-O hybridization and thereby drive the originally itinerant Ni e$_g$ electrons toward localization. This generates a local d8 (t$_{2g}$$^6$e$_g$$^2$) electronic configuration, leading to a Mott transition. In addition, we also find that compared with NdNiO$_3$, SmNiO$_3$ with a smaller A-site ionic radius more readily absorbs hydrogen but exhibits weaker proton diffusion capability. Hydrogenation promotes proton permeation along the [001] direction via the intraoctahedral transfer, whereas the overall proton diffusivity is reduced. These results provide guidance for experimental screening of strongly correlated oxides as electrolyte materials and offer theoretical insights for enhancing proton conductivity in rare-earth nickelates.

cond-mat.mtrl-sci↗

arXiv2026

Role of small-radius and high-electronegativity A-Site dopants in enhancing proton transport and stability of perovskite electrolytes

The practical application of BaCeO$_3$-based electrolytes is limited by their poor chemical stability in proton-conducting solid oxide fuel cells. Commonly employed B-site doping strategies typically improve proton transport with limited improvement in stability. Recent experiments show that A-site Ca doping can simultaneously enhance both properties. Here, through first-principles calculations and mechanistic analysis of Ca-doped BaCeO$_3$, we identify the synergistic roles of small-radius, high-electronegativity A-site dopants in governing proton transport and chemical stability in perovskite electrolytes. We show that the higher electronegativity of A-site dopant weakens the A-O ionic bonding, facilitating oxygen-vacancy formation and enhancing proton uptake by increasing the basicity. This weakened A-O interaction also suppresses the formation of impurity phases and reduces the adsorption strength of acidic gases such as CO$_2$ and SO$_2$. The lattice contraction induced by the smaller ionic radius improves thermal stability and can enhance proton diffusion in systems where proton transfer is the rate-limiting step. Furthermore, we find that Ca surface segregation can mitigate grain-boundary resistance effects. Our results demonstrate the advantages of A-site Ca doping in Ba-based electrolytes, clarify the mechanisms by which small-radius, high-electronegativity dopants influence proton transport and chemical stability, and provide guidance for the design of high-performance proton-conducting electrolytes.

cond-mat.mtrl-sci↗

arXiv2026

Search-Based Spatiotemporal and Multi-Robot Motion Planning on Graphs of Space-Time Convex Sets

Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained. We present an algorithmic framework based on graphs of space-time convex sets (ST-GCSs), where collision-free regions are represented as convex sets in space-time and trajectories correspond to paths on the graph together with continuous motions within the selected sets. We formulate time-optimal planning on ST-GCSs as a graph-search problem over path-indexed states and develop a best-first search solver that evaluates partial paths via continuous trajectory optimization, guided by admissible heuristics and dominance checks. We further present an Exact Convex Decomposition (ECD) scheme to reserve trajectory occupancies in space-time, enabling unified handling of dynamic obstacles and multi-robot interactions. For multi-robot motion planning, we integrate ST-GCS planning and ECD into prioritized planning methods and introduce a windowed coordination scheme to improve efficiency. Extensive experiments on single-robot and multi-robot problems demonstrate substantial speedups over various planners while maintaining high solution quality, particularly in environments with narrow and transient feasible regions. Large-scale demonstrations further show that the proposed multi-robot motion planner can solve instances with up to $100$ robots within only a few minutes. Project homepage: https://sites.google.com/view/stgcs

cs.RO↗

arXiv2026

CREST: Constraint-Release Execution for Multi-Robot Warehouse Shelf Rearrangement

Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it enforces strict trajectory dependencies, often leading to poor execution quality due to idle agents and unnecessary shelf switching. We introduce CREST, a new execution framework that achieves more continuous shelf carrying by proactively releasing trajectory constraints during execution. Experiments on diverse warehouse layouts show that CREST consistently outperforms MAPF-DECOMP, reducing metrics related to agent travel, makespan, and shelf switching by up to 40.5\%, 33.3\%, and 44.4\%, respectively, with even greater benefits under lift/place overhead. These results underscore the importance of execution-aware constraint release for scalable warehouse rearrangement. Code and data are available at https://github.com/ChristinaTan0704/CREST.

cs.RO↗

arXiv2025

Lattice-Distortion-Mediated Proton Pairing and Trapping in Solid State Oxides

Experiments have evidenced proton pairing in Y-doped BaZrO3. However, the nature of proton pairing and its impact on conduction remain insufficiently understood theoretically. Here, through quantitative computational analysis of proton-proton interactions in Y-doped BaZrO3, we identify lattice-distortion-mediated elastic interaction as the key factor determining whether two protons form a stable pair or exhibit net repulsion. When a proton resides at an inward-bending distortion site induced by another proton, the resulting net repulsive interaction leads to an unstable configuration. In contrast, the proton tends to be trapped at a nearby outward-bending site that favors the formation of a stable proton pair. Moreover, the site where the two protons form the lowest-energy configuration also corresponds to a proton trapping site. By calculating the long-range diffusion pathways accessible to protons under different local environments in both single- and two-proton cases, we find that the range of rate-limiting barriers is 0.24-0.45 eV for two-proton conduction and 0.19-0.39 eV for single-proton conduction. The higher and more experimentally consistent barriers in the two-proton pathways indicate that the proton trapping effect induced by pairing hinders proton conduction. Our study elucidates the multi-proton diffusion mechanism, providing a theoretical foundation for the experimental design of electrolytes with enhanced proton conductivity.

cond-mat.mtrl-sci↗

arXiv2025

GHOST: Solving the Traveling Salesman Problem on Graphs of Convex Sets

We study GCS-TSP, a new variant of the Traveling Salesman Problem (TSP) defined over a Graph of Convex Sets (GCS) -- a powerful representation for trajectory planning that decomposes the configuration space into convex regions connected by a sparse graph. In this setting, edge costs are not fixed but depend on the specific trajectory selected through each convex region, making classical TSP methods inapplicable. We introduce GHOST, a hierarchical framework that optimally solves the GCS-TSP by combining combinatorial tour search with convex trajectory optimization. GHOST systematically explores tours on a complete graph induced by the GCS, using a novel abstract-path-unfolding algorithm to compute admissible lower bounds that guide best-first search at both the high level (over tours) and the low level (over feasible GCS paths realizing the tour). These bounds provide strong pruning power, enabling efficient search while avoiding unnecessary convex optimization calls. We prove that GHOST guarantees optimality and present a bounded-suboptimal variant for time-critical scenarios. Experiments show that GHOST is orders-of-magnitude faster than unified mixed-integer convex programming baselines for simple cases and uniquely handles complex trajectory planning problems involving high-order continuity constraints and an incomplete GCS.

cs.AI↗

arXiv2025

Adaptation and Fine-tuning with TabPFN for Travelling Salesman Problem

Tabular Prior-Data Fitted Network (TabPFN) is a foundation model designed for small to medium-sized tabular data, which has attracted much attention recently. This paper investigates the application of TabPFN in Combinatorial Optimization (CO) problems. The aim is to lessen challenges in time and data-intensive training requirements often observed in using traditional methods including exact and heuristic algorithms, Machine Learning (ML)-based models, to solve CO problems. Proposing possibly the first ever application of TabPFN for such a purpose, we adapt and fine-tune the TabPFN model to solve the Travelling Salesman Problem (TSP), one of the most well-known CO problems. Specifically, we adopt the node-based approach and the node-predicting adaptation strategy to construct the entire TSP route. Our evaluation with varying instance sizes confirms that TabPFN requires minimal training, adapts to TSP using a single sample, performs better generalization across varying TSP instance sizes, and reduces performance degradation. Furthermore, the training process with adaptation and fine-tuning is completed within minutes. The methodology leads to strong solution quality even without post-processing and achieves performance comparable to other models with post-processing refinement. Our findings suggest that the TabPFN model is a promising approach to solve structured and CO problems efficiently under training resource constraints and rapid deployment requirements.

cs.LG↗

arXiv2025

In-depth Investigation of Conduction Mechanism on Defect-induced Proton-conducting Electrolytes BaHfO$_3$

This study utilizes first-principles computational methods to comprehensively analyze the impact of A-site doping on the proton conduction properties of BaHfO$_3$. The goal is to offer theoretical support for the advancement of electrolyte materials for solid oxide fuel cells. Our research has uncovered that BaHfO$_3$ demonstrates promising potential for proton conduction, with a low proton migration barrier of $0.28$ eV, suggesting efficient proton conduction can be achieved at lower temperatures. Through A-site doping, particularly with low-valence-state ions and the introduction of Ba vacancies, we can effectively decrease the formation energy of oxygen vacancies ($ E_{\text{vac}} $), leading to an increase in proton concentration. Additionally, our study reveals that the primary mechanism for proton migration in BaHfO$_3$ is the Grotthuss mechanism rather than the vehicle mechanism. Examination of the changes in lattice parameters during proton migration indicates that while doping or vacancy control strategies do not alter the mode of H$^+$ migration, they do influence the migration pathway and barrier. These findings provide valuable insights into optimizing the proton conduction properties of BaHfO$_3$ through A-site doping and lay a solid theoretical foundation for the development of novel, highly efficient solid oxide fuel cell electrolyte materials.

cond-mat.mtrl-sci↗

arXiv2025

Hydrogen Bond Strength Dictates the Rate-Limiting Steps of Diffusion in Proton-Conducting Perovskites:A Critical Length Perspective

Identifying the rate-limiting step of proton migration in proton-conducting oxides is essential for assessing and regulating proton conductivity. Proton migration based on the Grotthuss mechanism involves both proton rotation and proton transfer, with the latter typically regarded as the rate-limiting step. However, a universal criterion for identifying the rate-limiting step remains to be established. Here, we perform a quantitative decomposition of the rotation and transfer barriers, revealing that the hydrogen bond to the acceptor oxygen dictates their energy barrier difference via the O$_i$-B-O$_f$ bending mechanism. Based on the energy difference associated with a one-order-of-magnitude variation in residence time, we propose the hydrogen bond length criterion for identifying the rate-limiting step across operating temperatures. Taking the 500 K criterion as an upper limit, when the hydrogen-bond length of systems falls below 2.05~\AA, proton rotation becomes competitive with transfer. Applied to a wider range of perovskite materials, this criterion predicts comparable rotation and transfer rates in cubic structures with small lattice constants, low-valent B-site doped systems with moderate ionic radii, and distorted orthorhombic structures. Our findings provide an atomic-scale insight into the proton migration mechanisms in perovskites, and offer practical guidance for optimizing and designing advanced proton-conducting electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning

We address the Multi-Robot Motion Planning (MRMP) problem of computing collision-free trajectories for multiple robots in shared continuous environments. While existing frameworks effectively decompose MRMP into single-robot subproblems, spatiotemporal motion planning with dynamic obstacles remains challenging, particularly in cluttered or narrow-corridor settings. We propose Space-Time Graphs of Convex Sets (ST-GCS), a novel planner that systematically covers the collision-free space-time domain with convex sets instead of relying on random sampling. By extending Graphs of Convex Sets (GCS) into the time dimension, ST-GCS formulates time-optimal trajectories in a unified convex optimization that naturally accommodates velocity bounds and flexible arrival times. We also propose Exact Convex Decomposition (ECD) to "reserve" trajectories as spatiotemporal obstacles, maintaining a collision-free space-time graph of convex sets for subsequent planning. Integrated into two prioritized-planning frameworks, ST-GCS consistently achieves higher success rates and better solution quality than state-of-the-art sampling-based planners -- often at orders-of-magnitude faster runtimes -- underscoring its benefits for MRMP in challenging settings.

cs.RO↗

arXiv2024

Large-Scale Multirobot Coverage Path Planning on Grids With Path Deconfliction

We study Multi-Robot Coverage Path Planning (MCPP) on a 4-neighbor 2D grid G, which aims to compute paths for multiple robots to cover all cells of G. Traditional approaches are limited as they first compute coverage trees on a quadrant coarsened grid H and then employ the Spanning Tree Coverage (STC) paradigm to generate paths on G, making them inapplicable to grids with partially obstructed 2x2 blocks. To address this limitation, we reformulate the problem directly on G, revolutionizing grid-based MCPP solving and establishing new NP-hardness results. We introduce Extended-STC (ESTC), a novel paradigm that extends STC to ensure complete coverage with bounded suboptimality, even when H includes partially obstructed blocks. Furthermore, we present LS-MCPP, a new algorithmic framework that integrates ESTC with three novel types of neighborhood operators within a local search strategy to optimize coverage paths directly on G. Unlike prior grid-based MCPP work, our approach also incorporates a versatile post-processing procedure that applies Multi-Agent Path Finding (MAPF) techniques to MCPP for the first time, enabling a fusion of these two important fields in multi-robot coordination. This procedure effectively resolves inter-robot conflicts and accommodates turning costs by solving a MAPF variant, making our MCPP solutions more practical for real-world applications. Extensive experiments demonstrate that our approach significantly improves solution quality and efficiency, managing up to 100 robots on grids as large as 256x256 within minutes of runtime. Validation with physical robots confirms the feasibility of our solutions under real-world conditions.

cs.RO↗

arXiv2024

MFC-EQ: Mean-Field Control with Envelope Q-Learning for Moving Decentralized Agents in Formation

We study a decentralized version of Moving Agents in Formation (MAiF), a variant of Multi-Agent Path Finding aiming to plan collision-free paths for multiple agents with the dual objectives of reaching their goals quickly while maintaining a desired formation. The agents must balance these objectives under conditions of partial observation and limited communication. The formation maintenance depends on the joint state of all agents, whose dimensionality increases exponentially with the number of agents, rendering the learning process intractable. Additionally, learning a single policy that can accommodate different linear preferences for these two objectives presents a significant challenge. In this paper, we propose Mean-Field Control with Envelop $Q$-learning (MFC-EQ), a scalable and adaptable learning framework for this bi-objective multi-agent problem. We approximate the dynamics of all agents using mean-field theory while learning a universal preference-agnostic policy through envelop $Q$-learning. Our empirical evaluation of MFC-EQ across numerous instances shows that it outperforms state-of-the-art centralized MAiF baselines. Furthermore, MFC-EQ effectively handles more complex scenarios where the desired formation changes dynamically -- a challenge that existing MAiF planners cannot address.

cs.RO↗

arXiv2024

Reevaluation of Large Neighborhood Search for MAPF: Findings and Opportunities

Multi-Agent Path Finding (MAPF) aims to arrange collision-free goal-reaching paths for a group of agents. Anytime MAPF solvers based on large neighborhood search (LNS) have gained prominence recently due to their flexibility and scalability, leading to a surge of methods, especially those leveraging machine learning, to enhance neighborhood selection. However, several pitfalls exist and hinder a comprehensive evaluation of these new methods, which mainly include: 1) Lower than actual or incorrect baseline performance; 2) Lack of a unified evaluation setting and criterion; 3) Lack of a codebase or executable model for supervised learning methods. To address these challenges, we introduce a unified evaluation framework, implement prior methods, and conduct an extensive comparison of prominent methods. Our evaluation reveals that rule-based heuristics serve as strong baselines, while current learning-based methods show no clear advantage on time efficiency or improvement capacity. Our extensive analysis also opens up new research opportunities for improving MAPF-LNS, such as targeting high-delayed agents, applying contextual algorithms, optimizing replan order and neighborhood size, where machine learning can potentially be integrated. Code and data are available at https://github.com/ChristinaTan0704/mapf-lns-unified.

cs.RO↗

arXiv2024

Comparison of superconducting pairing in doped cuprates and nickelates within an extended Hubbard model

Within a Hubbard model, we investigate the superconducting pairing behavior of infinite-layer nickelate $\mathrm{NdNiO_2}$ and cuprate superconductors by using the determinant quantum Monte Carlo method. Our focus is on comparing their dominant pairing symmetries. The results indicate that the $d_{x^2-y^2}$ pairing interaction is significantly enhanced at low temperatures in both doped nickelates and cuprates, whereas other typical pairing symmetries are effectively suppressed, highlighting the dominance of the $d_{x^2-y^2}$ pairing form. Additionally, we find that the effective pairing interaction for $d_{x^2-y^2}$ pairing in doped nickelates is slightly lower than that in doped cuprates, which may be attributed to the different degrees of Fermi surface warping caused by the third-nearest hopping $t''$. Further studies show that the hole doping and interaction strength have significant effects on the $d_{x^2-y^2}$ pairing interaction within the selected parameter range. The $d_{x^2-y^2}$ pairing interaction is notably weakened when the hole doping increases, whereas it is significantly enhanced with increasing Coulomb interaction strength $U$. This comparative analysis reveals the similarities and differences in the pairing behaviors of doped nickelates and cuprates, which may provide further insights into understanding the superconducting properties of these two classes of materials.

cond-mat.str-el↗

arXiv2024

MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping

This paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time. Our method, MapTracker, accumulates a sensor stream into memory buffers of two latent representations: 1) Raster latents in the bird's-eye-view (BEV) space and 2) Vector latents over the road elements (i.e., pedestrian-crossings, lane-dividers, and road-boundaries). The approach borrows the query propagation paradigm from the tracking literature that explicitly associates tracked road elements from the previous frame to the current, while fusing a subset of memory latents selected with distance strides to further enhance temporal consistency. A vector latent is decoded to reconstruct the geometry of a road element. The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively. The code and models are available on our project page: https://map-tracker.github.io.

cs.CV↗

arXiv2024

Multi-Robot Connected Fermat Spiral Coverage

We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Connected Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time. MCFS uniquely enables the orchestration of multiple robots to generate coverage paths that contour around arbitrarily shaped obstacles, a feature that is notably lacking in traditional methods. Our framework not only enhances area coverage and optimizes task performance, particularly in terms of makespan, for workspaces rich in irregular obstacles but also addresses the challenges of path continuity and curvature critical for non-holonomic robots by generating smooth paths without decomposing the workspace. MCFS solves MCPP by constructing a graph of isolines and transforming MCPP into a combinatorial optimization problem, aiming to minimize the makespan while covering all vertices. Our contributions include developing a unified CFS version for scalable and adaptable MCPP, extending it to MCPP with novel optimization techniques for cost reduction and path continuity and smoothness, and demonstrating through extensive experiments that MCFS outperforms existing MCPP methods in makespan, path curvature, coverage ratio, and overlapping ratio. Our research marks a significant step in MCPP, showcasing the fusion of computer graphics and automated planning principles to advance the capabilities of multi-robot systems in complex environments. Our code is available at https://github.com/reso1/MCFS.

cs.AI↗