arXiv subjects

Peng Ren

Publications and source records attributed to Peng Ren.

At least 19 recordsLinked to original sources

arXiv2026

Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation

Vision-language-action policies are a promising foundation for general robot control, but long-horizon humanoid loco-manipulation requires the robot to treat task objects as persistent physical entities across movement, contact, occlusion, and recovery. We study this problem as object-state divergence: the object state used to condition a whole-body action can differ from the state used to decide whether the action achieved the intended physical relation. We propose \emph{Persistent Object Tokenization} (POT), which maintains role-indexed 3D object records from RGB-D observations and converts them into object tokens for a whole-body action expert. Instantiated as \emph{POT-VLA}, the same object records condition action generation and support geometric predicate checks, yielding a closed-loop execution system in which object state is both actionable and verifiable. On a Unitree G1, POT-VLA improves a matched direct GR00T-N1.7 baseline from 39/80 to 71/80 successes over eight real-world task families. In an external Being-0-aligned reference, POT-VLA achieves 44/50 successes on aligned service tasks, compared with the 37/50 success reported by the Being-0 paper. The largest gains occur on tasks requiring maintained 3D relations, suggesting that persistent object-centered state is a useful abstraction for verifiable humanoid VLA execution.

cs.RO↗

arXiv2026

Anti-Zariski pairs

In 1929, O. Zariski found a pair of complex plane algebraic curves of the same degree and with the same collection of singularities, but embedded into the plane in a topologically different way. Accordingly, such curves belong to different components of the equisingular family. This phenomenon has been intensively studied till now. In this note, we propose a different insight on this subject: Two curves $C',C''\subset\PP^2$ form an {\it anti-Zariski pair}, if $(\PP^2,C')$ and $(\PP^2,C'')$ are homeomorhic, but $C'$ and $C''$ belong to different components of the equisingular family. We exhibit examples of anti-Zariski pairs and discuss related issues.

math.AG↗

arXiv2026

EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control

Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-language-action systems provide semantic interfaces, but neither offers a scalable and interactive prior for broad full-body behavior. We introduce EgoPriMo (Egocentric Motion Prior for Humanoid Robots), a unified framework that learns such priors from egocentric human demonstrations. Given egocentric observations and a text prompt, EgoPriMo reconstructs, generates, and forecasts SMPL-based full-body motion. Language is used as a high-level control signal rather than a complete motion specification. At the core of EgoPriMo is a Triple-stream DiT that jointly models body dynamics, egocentric visual context, and text; task-conditioning masks route different tasks and missing-modality data through the same checkpoint. Experiments on Nymeria and EgoExo4D show that one checkpoint improves egocentric motion generation over UniEgoMotion while supporting reconstruction and forecasting; the generated SMPL motions can also be executed by a Unitree humanoid controller. These results indicate a practical path from scalable egocentric observations to generalizable and interactive humanoid motion priors.

cs.RO↗

arXiv2026

CyboRacket: A Perception-to-Action Framework for Humanoid Racket Sports

Dynamic ball-interaction tasks remain challenging for robots because they require tight perception-action coupling under limited reaction time. This challenge is especially pronounced in humanoid racket sports, where successful interception depends on accurate visual tracking, trajectory prediction, coordinated stepping, and stable whole-body striking. Existing robotic racket-sport systems often rely on external motion capture for state estimation or on task-specific low-level controllers that must be retrained across tasks and platforms. We present CyboRacket, a hierarchical perception-to-action framework for humanoid racket sports that integrates onboard visual perception, physics-based trajectory prediction, and large-scale pre-trained whole-body control. The framework uses onboard cameras to track the incoming object, predicts its future trajectory, and converts the estimated interception state into target end-effector and base-motion commands for whole-body execution by SONIC on the Unitree G1 humanoid robot. We evaluate the proposed framework in a vision-based humanoid tennis-hitting task. Experimental results demonstrate real-time visual tracking, trajectory prediction, and successful striking using purely onboard sensing.

cs.RO↗

arXiv2026

Cybo-Waiter: A Physical Agentic Framework for Humanoid Whole-Body Locomotion-Manipulation

Robots are increasingly expected to execute open ended natural language requests in human environments, which demands reliable long horizon execution under partial observability. This is especially challenging for humanoids because locomotion and manipulation are tightly coupled through stance, reachability, and balance. We present a humanoid agent framework that turns VLM plans into verifiable task programs and closes the loop with multi object 3D geometric supervision. A VLM planner compiles each instruction into a typed JSON sequence of subtasks with explicit predicate based preconditions and success conditions. Using SAM3 and RGB-D, we ground all task relevant entities in 3D, estimate object centroids and extents, and evaluate predicates over stable frames to obtain condition level diagnostics. The supervisor uses these diagnostics to verify subtask completion and to provide condition-level feedback for progression and replanning. We execute each subtask by coordinating humanoid locomotion and whole-body manipulation, selecting feasible motion primitives under reachability and balance constraints. Experiments on tabletop manipulation and long horizon humanoid loco manipulation tasks show improved robustness from multi object grounding, temporal stability, and recovery driven replanning.

cs.RO↗

arXiv2026

OilSAM2: Memory-Augmented SAM2 for Scalable SAR Oil Spill Detection

Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segmentation, existing SAM based approaches operate on single images and cannot effectively reuse information across scenes. Memory augmented variants (e.g., SAM2) further assume temporal coherence, making them prone to semantic drift when applied to unordered SAR image collections. We propose OilSAM2, a memory augmented segmentation framework tailored for unordered SAR oil spill monitoring. OilSAM2 introduces a hierarchical feature aware multi scale memory bank that explicitly models texture, structure, and semantic level representations, enabling robust cross image information reuse. To mitigate memory drift, we further propose a structure semantic consistent memory update strategy that selectively refreshes memory based on semantic discrepancy and structural variation.Experiments on two public SAR oil spill datasets demonstrate that OilSAM2 achieves state of the art segmentation performance, delivering stable and accurate results under noisy SAR monitoring scenarios. The source code is available at https://github.com/Chenshuaiyu1120/OILSAM2.

cs.CV↗

arXiv2026

Accurate Pedestrian Tracking in Urban Canyons: A Multi-Modal Fusion Approach

The contribution describes a pedestrian navigation approach designed to improve localization accuracy in urban environments where GNSS performance is degraded, a problem that is especially critical for blind or low-vision users who depend on precise guidance such as identifying the correct side of a street. To address GNSS limitations and the impracticality of camera-based visual positioning, the work proposes a particle filter based fusion of GNSS and inertial data that incorporates spatial priors from maps, such as impassable buildings and unlikely walking areas, functioning as a probabilistic form of map matching. Inertial localization is provided by the RoNIN machine learning method, and fusion with GNSS is achieved by weighting particles based on their consistency with GNSS estimates and uncertainty. The system was evaluated on six challenging walking routes in downtown San Francisco using three metrics related to sidewalk correctness and localization error. Results show that the fused approach (GNSS+RoNIN+PF) significantly outperforms GNSS only localization on most metrics, while inertial-only localization with particle filtering also surpasses GNSS alone for critical measures such as sidewalk assignment and across street error.

cs.RO↗

arXiv2025

LILAC: Long-sequence Incremental Low-latency Arbitrary Motion Stylization via Streaming VAE-Diffusion with Causal Decoding

Generating long and stylized human motions in real time is critical for applications that demand continuous and responsive character control. Despite its importance, existing streaming approaches often operate directly in the raw motion space, leading to substantial computational overhead and making it difficult to maintain temporal stability. In contrast, latent-space VAE-Diffusion-based frameworks alleviate these issues and achieve high-quality stylization, but they are generally confined to offline processing. To bridge this gap, LILAC (Long-sequence Incremental Low-latency Arbitrary Motion Stylization via Streaming VAE-Diffusion with Causal Decoding) builds upon a recent high-performing offline framework for arbitrary motion stylization and extends it to an online setting through a latent-space streaming architecture with a sliding-window causal design and the injection of decoded motion features to ensure smooth motion transitions. This architecture enables long-sequence real-time arbitrary stylization without relying on future frames or modifying the diffusion model architecture, achieving a favorable balance between stylization quality and responsiveness as demonstrated by experiments on benchmark datasets. Supplementary video and examples are available at the project page: https://pren1.github.io/lilac/

cs.CV↗

arXiv2025

DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph

Current general-purpose large language models (LLMs) commonly exhibit knowledge hallucination and insufficient domain-specific adaptability in domain-specific tasks, limiting their effectiveness in specialized question answering scenarios. Retrieval-augmented generation (RAG) effectively tackles these challenges by integrating external knowledge to enhance accuracy and relevance. However, traditional RAG still faces limitations in domain knowledge accuracy and context modeling.To enhance domain-specific question answering performance, this work focuses on a graph-based RAG framework, emphasizing the critical role of knowledge graph quality during the generation process. We propose DSRAG (Domain-Specific RAG), a multimodal knowledge graph-driven retrieval-augmented generation framework designed for domain-specific applications. Our approach leverages domain-specific documents as the primary knowledge source, integrating heterogeneous information such as text, images, and tables to construct a multimodal knowledge graph covering both conceptual and instance layers. Building on this foundation, we introduce semantic pruning and structured subgraph retrieval mechanisms, combining knowledge graph context and vector retrieval results to guide the language model towards producing more reliable responses. Evaluations using the Langfuse multidimensional scoring mechanism show that our method excels in domain-specific question answering, validating the efficacy of integrating multimodal knowledge graphs with retrieval-augmented generation.

cs.IR↗

arXiv2025

OSDMamba: Enhancing Oil Spill Detection from Remote Sensing Images Using Selective State Space Model

Semantic segmentation is commonly used for Oil Spill Detection (OSD) in remote sensing images. However, the limited availability of labelled oil spill samples and class imbalance present significant challenges that can reduce detection accuracy. Furthermore, most existing methods, which rely on convolutional neural networks (CNNs), struggle to detect small oil spill areas due to their limited receptive fields and inability to effectively capture global contextual information. This study explores the potential of State-Space Models (SSMs), particularly Mamba, to overcome these limitations, building on their recent success in vision applications. We propose OSDMamba, the first Mamba-based architecture specifically designed for oil spill detection. OSDMamba leverages Mamba's selective scanning mechanism to effectively expand the model's receptive field while preserving critical details. Moreover, we designed an asymmetric decoder incorporating ConvSSM and deep supervision to strengthen multi-scale feature fusion, thereby enhancing the model's sensitivity to minority class samples. Experimental results show that the proposed OSDMamba achieves state-of-the-art performance, yielding improvements of 8.9% and 11.8% in OSD across two publicly available datasets.

cs.CV↗

arXiv2025

Localization behavior in a Hermitian and non-Hermitian Raman lattice

We propose a flexible Raman lattice system for alkaline-earth-like atoms to theoretically investigate localization behaviors in a quasi-periodic lattice with controllable non-Hermiticity. Our analysis demonstrates that critical phases and mobility edges can arise by adjusting spin-dependence of the incommensurate potentials in the Hermitian regime. With non-Hermiticity introduced by spin-selective atom loss, our calculations reveal that critical localization behaviour in this system can be suppressed by dissipation. Our work provides insights into interplay between quasi-periodicity and non-Hermitian physics, offering a new perspective on localization phenomena.

cond-mat.quant-gas↗

arXiv2025

Determining Linker Ratios of Mixed Metal-Organic Frameworks via Magnetic Susceptibility Measurements

Partial replacement of the organic linkers of metal-organic frameworks (MOFs) often optimizes their functionalities, however, accurate characterization of their molar ratios in many cases is challenging. This work presents a method of determining such linker ratios via measurements of the magnetic susceptibility of small quantities of powdered samples. The main presumption is taking the diamagnetic and paramagnetic contributions to the molar magnetic susceptibility of the two parent MOFs to be additive. To verify this, four examples are provided with commonly used MOFs to represent the cases when both parent MOFs are either paramagnetic or diamagnetic but with different linkers with the following systems: [MIL-101(Cr)-SO$_3$H]$_{(1-\delta)}$[MIL-101(Cr)-NO$_2$]$_{\delta}$, [EuMOF]$_{(1-\delta)}$[EuPDCA]$_{\delta}$, [UiO-66-COOH]$_{(1-\delta)}$[UiO-66]$_{\delta}$ and [MIL-101(Cr) F Free]$_{(1-\delta)}$[MIL-53(Al)]$_{\delta}$, where 1-$\delta$ : $\delta$ are the ratios to be determined. Depending on whether the systems were strictly paramagnetic, strictly diamagnetic or mixed, the experimental error of $\delta$ ranged between 0.00002 and 0.012, respectively. We expect the presented method to be widely employed since samples only need to be in powdered form and because there is a lack of characterization tools in the area of MOF linker ratios. The presented method is also applicable to resolving the ratios of mixed ordinary paramagnetic systems as well as other types of non-magnetic composite materials such as tapes, zeolites and thin films.

cond-mat.mtrl-sci↗

arXiv2025

Colossal Dielectric Response and Electric Polarization in Lithium Nitrate

Lithium nitrate LiNO$_3$ is identified to possess a dielectric constant $\epsilon$' larger than 6x10$^6$ at 1 kHz in powder samples above the critical temperature $T$$_W$ = 306 K. For single crystalline samples, $\epsilon$' can be sustained to remain above 10$^5$ and the dissipation factor below 10 in the temperature region of 280-340 K after a simple 'activation' process. Moreover, pyroelectric current measurements show LiNO$_3$ to be ferroelectric with an electric polarization of $P$ = 1,200 $\mu$C/cm$^2$. Both $\epsilon$' and $P$ are amongst one of the highest in all known materials. We propose a model suggesting the mechanism underlying the colossal magnitudes of $\epsilon$' and $P$ to stem from a gearing-ungearing process of the planar NO$_3$$^-$ at the macroscopic level. Our results potentially push the boundaries of ceramic capacitors.

cond-mat.mtrl-sci↗

arXiv2025

Magnetism based on nitrate-nitrate interactions: The cases of LiNO$_3$, K$_{0.5}$Rb$_{0.5}$NO$_3$, Ca(NO$_3$)$_2$ and C(NH$_2$)$_3$NO$_3$

Long-range magnetic ordering of the orbital motion of oxygen atoms within NO$_3$$^-$ cations is identified from experimental measurements of the magnetic susceptibility $\chi$($T$) in LiNO$_3$, Ca(NO$_3$)$_2$, K$_{0.5}$Rb$_{0.5}$NO$_3$ and C(NH$_2$)$_3$NO$_3$ at their respective order-disorder, solid-solid phase transitions $T$$_N$. The observed sharp changes in $\chi$($T$) and accompanying hysteretic behavior indicate the phase transitions to be first order. A model employing the law of conservation of angular momentum is used to explain why the librations between neighboring NO$_3$$^-$ become geared below $T$$_N$. Since the periodic motions involve concerted motion of net charges, the associated magnetic moments of the NO$_3$$^-$ ions indirectly establish an antiferromagnetic structure below $T$$_N$. Our findings identify a previously unidentified type of molecular interaction which may be exploited to further increase the enthalpy of the widely-popular hydrated salts employed as energy storage devices.

cond-mat.mtrl-sci↗

arXiv2025

Gearing of nitrate ions in ammonium nitrate

Reorienting polyatomic ions such as NH4+ and NO3- exhibit weak magnetic fields because the ions at the extremities trace out current loops; if the periodic reorientations become long-range ordered (i.e. gearing of neighboring NO3-), then the magnetic susceptibility should exhibit a unique signature along the different crystallographic axes. For the case of ammonium nitrate NH4NO3, we report the presence of two successive sharp steps in the molar magnetic susceptibility along the a- and b-axes upon crossing its order-disorder phase transition (from phase IV to phase II). We suggest the first step pertains to the NO3- planes shifting away from facing only along the b-axis and onto the a-axis by 45{\deg}. The second step is attributed to the disordering (ungearing) of the NH4+ and NO3-. In contrast, only one step was observed in the magnetic susceptibility along the c-axis and its large magnitude suggest the NO3- remain weakly correlated even in phase I at 400 K. We also find evidence that the NH4+ become magnetically ordered (geared) along the c-axis only until phase V. The approach employed in this work can be extended to experimentally study the lattice dynamics of other solids possessing planar ions such as amphidynamic crystals.

physics.chem-ph↗

arXiv2024

Third-Order Exceptional Point in Non-Hermitian Spin-Orbit-Coupled cold atoms

Exceptional points (EPs) has seen substantial advances in both experiment and theory. However, in quantum systems, higher-order exceptional points remain of great interest and possess numerous intriguing properties yet to be fully explored. Here, we describe a \emph{PT} symmetry-protected three-level non-Hermitian system with the dissipative spin-orbit-coupled (SOC) fermions in which a third-order exceptional point (EP3) emerges when both the eigenvalues and eigenstates of the system collapse into one. The band structure and its spin dynamics are explored for $^{173}$Yb fermions. We highlight the enhanced sensitivity to the external perturbation of EP3 with cubic-root energy dispersion. Additionally, we investigate the second-order exceptional point (EP2) with square-root energy dispersion in a three-level quantum system with the absence of parity symmetry, which proves that the enhanced sensitivity closely relates to the symmetries of the NH system. Furthermore, we analyze the encircling behavior of EP3 in terms of the adiabatic limit and the nonadiabatic dynamics and discover some different results from that of EP2.

cond-mat.quant-gas↗

arXiv2024

Stability of syzygy bundles on varieties of Picard number one

We give a criterion for slope-stability of the syzygy bundle of a globally generated ample line bundle on a smooth projective variety of Picard number $1$ in terms of Hilbert polynomial. As applications, we prove the stability of syzygy bundles on many varieties, such as smooth Fano or Calabi--Yau complete intersections, hyperk\"ahler varieties of Picard number 1, abelian varieties of Picard number $1$, rational homogeneous varieties of Picard number 1, weak Calabi--Yau varieties of Picard number $1$ of dimension $\leq 4$, and Fano varieties of Picard number $1$ of dimension $\leq5$. Also we prove the stability of syzygy bundles on all hyperk\"ahler varieties.

math.AG↗

arXiv2024

Data-Driven Cooperative Output Regulation of Continuous-Time Multi-Agent Systems with Unknown Network Topology

This paper investigates data-driven cooperative output regulation for continuous-time multi-agent systems with unknown network topology. Unlike existing studies that typically assume a known network topology to directly compute controller parameters, a novel approach is proposed that allows for the computation of the parameter without prior knowledge of the topology. A lower bound on the minimum non-zero eigenvalue of the Laplacian matrix is estimated using only edge weight bounds, enabling the output regulation controller design to be independent of global network information. Additionally, the common need for state derivative measurements is eliminated, reducing the amount of data requirements. Furthermore, necessary and sufficient conditions are established to ensure that the data are informative for cooperative output regulation, leading to the design of a distributed output regulation controller. For the case with noisy data, the bound of the output error is provided, which is positively correlated with the noise bound, and a distributed controller is constructed for the approximate cooperative output regulation. Finally, the effectiveness of the proposed methods is verified through numerical simulations.

cs.MA↗