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Manipulation

Publish Date	Title & Abstract	Authors	Links
2026-06-09	TacForeSight: Force-Guided Tactile World Model for Contact-Rich Manipulation Dexterous Manipulation Tactile Contact-rich manipulation requires robots to continuously perceive and regulate evolving physical interactions under dynamic contact transitions or complex surface geometries. Recent imitation learning methods improve contact-aware control by incorporating tactile or force feedback, but they rarely model the asymmetric spatiotemporal roles of global force and local tactile sensing. To address…	Wenchao Ding Team	ArXiv
2026-06-09	Task Robustness via Re-Labelling Vision-Action Robot Data Dexterous Manipulation VLA The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios. However, these policies continue to struggle with following instructions, likely due to the limited linguistic and action sequence diversity in existing robotics datasets. This paper introduces Task Robustness via Re-Labelling…	Glen Berseth Team	ArXiv / Web
2026-06-09	MV-Actor: Aligning Multi-View Semantics and Spatial Awareness for Bimanual Manipulation Dexterous Manipulation Robotic manipulation has been widely applied in industrial scenarios. Compared with single-arm manipulation, bimanual manipulation is equipped with multiple cameras to capture information from different viewpoints. However, existing multi-view policies encode each view independently or fuse view features shallowly, resulting in limited sharing semantic perception and unreliable spatial awareness….	You Yang Team	ArXiv
2026-06-09	IMPACT: Learning Internal-Model Predictive Control for Forceful Robotic Manipulation Dexterous Manipulation Tactile Real-world robotic manipulation tasks often involve forceful interactions with the environment, such as using tools of varying weights, transporting objects with different masses, and performing contact-rich tasks like table wiping. Previous learning-based approaches typically employ imitation learning policies that output target end-effector poses tracked by low-level impedance controllers. In…	Yilun Du Team	ArXiv / Web
2026-06-09	Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization Dexterous Manipulation Learning from human video demonstrations remains challenging due to noisy hand-object interactions, unseen objects with partial observation, and cross-embodiment discrepancy. To address these challenges, we present \textit{HOWTransfer} (\emph{H}and-\emph{O}bject \emph{O}pen-\emph{W}orld Transfer), a hand-centric framework that distills human demonstrations into contact-aware, taxonomy-informed,…	Rania Rayyes Team	ArXiv
2026-06-09	Dexterous Point Policy: Learning Point-based Dexterous Hand Policies from Human Demonstrations Dexterous Manipulation VLA Robotic foundation models pre-trained on human demonstration videos have shown promise, but a significant embodiment gap remains when the resulting policies are deployed on real robots. A common remedy is to fine-tune these models on robot-specific demonstrations. However, robot data collection can be prohibitively expensive and time-consuming, which is particularly acute in dexterous…	Jinwoo Shin Team	ArXiv
2026-06-09	VeriSpace: Spatially Grounded Action Verification for Vision-Language-Action Models Dexterous Manipulation VLA Vision-language-action (VLA) models have shown strong promise for robotic manipulation, but their reliability at test time remains limited by one-shot action prediction, where even small action errors can cause grasp failure, collision, or incorrect task progression. A natural alternative is to equip VLA systems with test-time verification, allowing multiple candidate actions to be proposed and…	Jing Liu Team	ArXiv
2026-06-09	Derived skein module Manipulation We propose a model-independent axiomatic framework for the derived skein theory of oriented 3-manifolds with coefficients in a ribbon tensor category, especially focusing on the case where the input category is the category of finite-dimensional representations of a quantum group with quantum parameter not a root of unity. The axioms are designed so that the 0th homology recovers the ordinary…	Chun-Yu Bai	ArXiv
2026-06-09	Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning Manipulation Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control. While they are known to scale stably in the supervised imitation learning setting, incorporating them into reinforcement learning (RL) pipelines for policy improvement has proven more difficult. It often requires…	Sergey Levine Team	ArXiv
2026-06-09	Endpoint Logarithms in the NLO Mueller-Navelet Jet Vertex: Threshold Matching and BLM/MOM Prescription Sensitivity Manipulation The endpoint region $ζ\to1$ of the NLO forward jet vertex has not been systematically separated from BFKL energy-scale terms in Mueller-Navelet phenomenology. Starting from the small-cone NLO vertex, we isolate the quark and gluon plus distributions and construct a BFKL-aware threshold matching scheme that preserves exact NLO accuracy. The conservative Scheme-II exponent resums only the ordinary…	Lei Wang	ArXiv
2026-06-09	An Uncertainty Estimation Framework for Dose Accumulation in Adaptive Radiotherapy: Application to CBCT-Guided Radiotherapy for Cervical Cancer Manipulation Background and purpose: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited by DIR, segmentation, and anatomical uncertainties. We introduce IMPACT-DoseAcc, an uncertainty-aware dose accumulation framework, within IMPACT for semantic feature-driven image analysis. The framework is modality- and disease-agnostic and is applied…	Caroline Lafond Team	ArXiv
2026-06-09	Spontaneous polarization for protrusion-driven cell crawling Manipulation We propose a minimal one-dimensional continuum model for the spontaneous initiation of protrusion-driven cell crawling on a rigid substrate. The cell cytoskeleton is represented as a viscous actin meshwork that turns over in the bulk and polymerizes at two moving cell edges. Symmetry breaking arises from the feedback between cell motion, an external chemical regulator of actin nucleation, and…	Pierre Recho	ArXiv
2026-06-09	MODIP: Efficient Model-Based Optimization for Diffusion Policies Manipulation Diffusion policies (DPs) have emerged as expressive policy representations for robot learning, often used with imitation learning methods such as behavioral cloning (BC). However, while their success has largely been confined to BC, direct reinforcement learning (RL) fine-tuning remains challenging because actions are generated through a multi-step denoising process. In this work, we propose…	Olivier Sigaud Team	ArXiv
2026-06-09	Nonlinear Anisotropic Visco-Anelasticity Manipulation We formulate a nonlinear geometric theory of visco-anelasticity that unifies viscoelastic and anelastic responses within a single thermodynamic framework. At each material point, the total deformation gradient is multiplicatively decomposed into elastic, viscous, and anelastic distortions, thereby generalizing the Bilby-Kröner-Lee decomposition to visco-anelasticity. The theory explicitly…	Arash Yavari Team	ArXiv
2026-06-09	Pair creation amplitudes for a real scalar field coupled to a time-dependent surface in d+1 dimensions Manipulation We study the pair creation phenomenon for a real scalar field $\varphi$ in the presence of a surface that undergoes time-dependent deformations, while imposing Dirichlet-like boundary conditions. Including terms up to fourth order in the departure of the surface from an infinite plane, we present results for the angular dependence of the emission rate for the vacuum-to-pair process as a function…	B. C. Guntsche Team	ArXiv
2026-06-09	Don’t waste SAM Manipulation Meta AI has recently released the Segment Anything Model (SAM), which demonstrates exceptional zero-shot image segmentation performance across various tasks with remarkable accuracy. Despite its inability to provide accurate segmentation across multiple research fields, SAM still serves as a valuable starting point for supporting the segmentation pipeline process, particularly for tasks that…	Uwe Handmann Team	ArXiv
2026-06-09	Frenet turns Manipulation We discuss a problem posed by A.~Agrachev asking how many times a usual circle in $\mathbb R^n$ should be traversed to admit a deformation by curves with nowhere degenerating Frenet frame. It turns out that the answer depends on a specific topology which we consider. For the literal $C^n$ curve topology, the least number of turns of a plane circle admitting arbitrarily small nondegenerate…	Boris Shapiro	ArXiv
2026-06-09	Structured deformations for energies with general surface terms Manipulation We develop a variational theory of structured deformations for energies whose surface densities satisfy general growth conditions. This requires a formulation in the generalised space ${\rm GBV}_\star$, introduced by Dal Maso and Toader, which is the natural setting for surface energies that are linear near the origin and bounded at infinity. In this framework, we prove three main results: an…	Manuel Friedrich Team	ArXiv
2026-06-09	A phase-field modeling approach to sea-ice fracturing Manipulation The thin ice that covers the polar oceans is a complex geomaterial that is constantly stressed and fractured by winds and ocean currents. In the central Arctic, this forcing produces deformations in the form of shear bands, within which individual ice plates detach, locally generating a granular medium. Capturing this transition from a continuous to a granular sea-ice cover has implications for…	Véronique Dansereau Team	ArXiv
2026-06-09	LAFP: Preserving Latent Action Structure in Latent Policy Learning via Flow Matching Manipulation Learning high-quality latent actions from large-scale unlabeled videos, coupled with limited real-world interaction data for training an action decoder, has emerged as a promising paradigm for scalable latent policy learning. However, existing approaches typically rely on behavior cloning, which tends to collapse inherently multimodal action distributions into unimodal ones, thereby degrading the…	Wei Li Team	ArXiv
2026-06-08	MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models Dexterous Manipulation VLA Temporal modeling is essential for robotic manipulation, as effective control requires both memory of past interactions and imagination of future states. However, most VLA models rely primarily on the current observation and therefore struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived context, the hippocampal…	Gao Huang Team	ArXiv / Web
2026-06-08	AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing Dexterous Manipulation World-action models have emerged as a promising paradigm for robot manipulation, jointly modeling visual scene dynamics and actions to inject physical priors into policy learning. However, existing world-action models couple world prediction and action execution at the same temporal resolution, forcing the world branch to model near-term frame variations that are redundant and weakly informative….	Yao Mu Team	ArXiv / Web
2026-06-08	AetheRock: An Arm-Worn Robot Teaching System for Force-Guided Vision-Tactile Learning Dexterous Manipulation Tactile Force and tactile sensing are indispensable in contact-rich manipulation. However, force-aware robot learning faces critical challenges due to the incompatible assembly of tactile and force sensors in handheld or wearable devices. To address these limitations, we first introduce AetheRock for gripper-force, vision, and tactile data collection, which is an arm-worn device featuring a modular and…	Yong-Lu Li Team	ArXiv
2026-06-08	Difference-Aware Retrieval Policies for Imitation Learning Dexterous Manipulation Parametric imitation learning via behavior cloning can suffer from poor generalization to out-of-distribution states due to compounding errors during deployment. We show that reusing the training data during inference via a semi-parametric retrieval-based imitation learning approach can alleviate this challenge. We present Difference-Aware Retrieval Policies for Imitation Learning (DARP), a…	Abhishek Gupta Team	ArXiv / Web
2026-06-08	Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models Dexterous Manipulation VLA Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these policies offer no guarantees against collisions with task-irrelevant objects in the scene. Existing safety filters sidestep this problem by querying a vision-language model (VLM) to identify obstacles and their locations. This, however, is too slow…	Nader Sehatbakhsh Team	ArXiv
2026-06-08	ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models Dexterous Manipulation VLA Vision-Language-Action (VLA) models demonstrate strong perfor-1 mance on language-conditioned robotic manipulation within their training dis-2 tribution, yet their generalization capabilities remain fundamentally limited. They3 lack the robustness required to handle perturbations, frequently failing when con-4 fronted with lighting changes, altered camera viewpoints, or small initial-state5…	Nader Sehatbakhsh Team	ArXiv
2026-06-08	ReCoVLA: VLM-Guided Reward Compilation for Failure Recovery in Vision-Language-Action Policies Dexterous Manipulation VLA Sim2Real Vision-language-action (VLA) policies provide strong priors for language-conditioned manipulation, but remain brittle in off-nominal states requiring targeted recovery. We propose ReCoVLA – a failure-conditioned residual recovery framework that keeps a pretrained VLA policy frozen, uses an external vision-language model (VLM) to infer the failure mode and recovery stage, and compiles a…	Toshiaki Koike-Akino Team	ArXiv
2026-06-08	DexPIE: Stable Dexterous Policy Improvement from Real-World Experience Dexterous Manipulation Dexterous manipulation presents substantial challenges for imitation learning due to its high-dimensional action space and complex contact-rich dynamics. Policies trained purely from demonstrations often suffer from compounding errors during deployment and require large amounts of expert data to achieve reliable performance. To move beyond the limitations of demonstration data, in this work, we…	Yaonan Wang Team	ArXiv / Web
2026-06-08	CT-VAM: A Cerebello-Thalamic-Inspired Vision-Action Model for Efficient Visuomotor Control Dexterous Manipulation VLA Vision-language-action models have shown strong promise for robot manipulation, yet raw language is primarily needed to specify task intent rather than to be repeatedly processed during high-frequency low-level execution. Motivated by this separation, we propose a cerebello-thalamic-inspired vision-action model (CT-VAM) for efficient task-conditioned visuomotor control. CT-VAM acts as a compact…	Jiahu Qin Team	ArXiv
2026-06-08	Topological Triplons in the Pinwheel Valence Bond Solid on the Kagome Lattice Manipulation We investigate the triplon excitations of the pinwheel valence-bond-solid phase on the deformed kagome lattice compound Rb2Cu3SnF12. Using bond-operator mean-field theory, we compute the triplon band structure, dynamical structure factor, Berry curvatures and the associated thermal Hall response. We show that the presence of Dzyaloshinskii-Moriya interactions and an external magnetic field are…	Johannes Knolle Team	ArXiv
2026-06-08	Linear Ricci-Trace Deformations and Operational Equivalence in Rastall-Type Gravity Manipulation We analyze a class of linear Ricci–trace deformations of Einstein’s field equations in which the relative weight between the Ricci tensor and the scalar-curvature trace sector is modified while the metric remains the only gravitational field. The purpose of the analysis is structural rather than phenomenological: we classify the corresponding field-equation class, fix the parameter dictionaries…	Marcelo H. Alavarenga Team	ArXiv
2026-06-08	A fast and consistent sharp-interface immersed boundary method for moving bodies of arbitrary thicknes Manipulation Immersed boundary methods (IBMs) are widely used to simulate flows around complex geometries and moving bodies, but they often involve a trade-off between precision and computational efficiency. Eulerian formulations require special treatments for moving walls and may generate spurious force oscillations, whereas Lagrangian formulations can suffer from slip errors at the immersed surfaces. We…	Francesco Viola Team	ArXiv
2026-06-08	Double-Current Deformations of Two-Dimensional QFTs with Anomalies Manipulation We construct the double-current deformations of two-dimensional quantum field theories whose partition functions have background gauge-field anomalies. Extending the path integral construction of [1], we couple the seed theory to dynamical gauge fields and compact Stueckelberg fields and insert parallel transport in the anomaly line bundle. The deformed partition function then has the same…	Zhengyuan Du	ArXiv
2026-06-08	Lifting Effective-Field-Theory Degeneracies in Semileptonic Heavy-Baryon Decays Manipulation Semileptonic heavy-baryon decays provide a sensitive probe of the helicity structure underlying possible lepton-flavor universality violation in $b\to c\,τ\barν_τ$ transitions. We perform an effective-field-theory analysis of $Λ_b\toΛ_cτ\barν_τ$ and related baryonic modes using lattice-QCD helicity form factors with full covariance propagation. Propagating meson-compatible EFT solutions into the…	Hindi Zouhair	ArXiv
2026-06-08	Reduced integration with scaled boundary parametrization for virtual elements at finite strains Manipulation This contribution presents an alternative stabilization technique for the virtual element method (VEM) based on reduced integration combined with a scaled boundary parametrization. To this end, a Taylor series expansion of the constitutive quantities with respect to the sectional center is carried out, enabling analytical integration of the weak form and reducing the need for integration points…	Hagen Holthusen Team	ArXiv
2026-06-08	Control problem in millimeter-wave adaptive optics Manipulation Millimeter-wave Adaptive Optics (MAO) is essential for high-precision large-aperture submillimeter telescopes, requiring real-time compensation of wavefront errors by capturing them as spatially-discrete excess path length (EPL) fluctuations. This paper presents a unified control-theoretic framework for the EPL compensation problem. We first model the optical drive system as a plant where input…	Yoichi Tamura Team	ArXiv
2026-06-08	Targeting World Models to Compromise Robot Learning Pipelines Manipulation VLA World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline. While highly practical, in this work we demonstrate that world models introduce a uniquely stealthy and effective data poisoning…	Eugene Bagdasarian Team	ArXiv
2026-06-08	Goal Sets, Not Goal States: Queryable Robot Goals through Goal-Set Hindsight Relabeling Manipulation Hindsight relabeling usually turns achieved future states into exact goals, which can overconstrain offline robot learning when task success depends only on a subset of the state. We propose Goal-Set Hindsight Relabeling (GS-HER), a predicate-level generalization of HER in which achieved states certify query-defined goal sets rather than singleton goal states. A binary query specifies which…	Jorge Pomares Team	ArXiv
2026-06-08	Leveraging Morphology for Historical Script Metrological Analysis Manipulation Advances in handwritten text recognition have enabled large-scale transcription of historical documents, but still provide limited access to interpretable visual measurements for paleography, the study of historical scripts. In this paper, our main insight is that morphological script analysis, in particular the capacity to learn character prototypes from line-level transcriptions, enables the…	Mathieu Aubry Team	ArXiv
2026-06-08	A self-consistent EOB–Teukolsky framework for generic extreme mass-ratio inspirals Manipulation We present a full-relativistic waveform model for extreme mass-ratio inspirals (EMRIs) by self-consistently combining the effective one-body (EOB) formalism with the Teukolsky equation. The model incorporates analytical, mass-ratio-informed geodesic solutions within a deformed Kerr metric into the source term of the Teukolsky equation, establishing a direct connection between finite-mass-ratio…	Xaobo Zou Team	ArXiv
2026-06-05	Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping Dexterous Manipulation Sim2Real Biosignals-free shared-autonomy control of upper-limb prosthetic hands aims to enable natural and low-effort manipulation without relying on EMG or other physiological signals. Recent imitation-learning-based approaches have shown promising results, but their scalability is limited by the cost and variability of collecting large amounts of real-world human demonstration data. In this work, we…	Xianta Jiang Team	ArXiv
2026-06-05	Spline Policy: A Structured Representation for Robot Policies Dexterous Manipulation VLA Modern imitation-learning policies for robot manipulation often represent actions as fixed-resolution action chunks, which are simple and effective but expose limited geometric and temporal structure before execution. This paper studies Spline Policy (SP), a structured representation that replaces action chunks with spline parameters while keeping the policy backbone unchanged. The predicted…	Sylvain Calinon Team	ArXiv
2026-06-05	RhinoVLA Technical Report Dexterous Manipulation VLA Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging. In this work, we identify VLM visual and context tokens as a major source of deployment latency: for GEMM-dominated projection operators, computation grows linearly with the number of input tokens when model dimensions are fixed. Motivated by…	Yuxi Liu Team	ArXiv
2026-06-05	Robotic Policy Adaptation via Weight-Space Meta-Learning Dexterous Manipulation VLA HF-Hot Vision-Language-Action (VLA) models are emerging as a promising paradigm for robotic manipulation, enabling general-purpose policies trained from large corpora of demonstrations and action labels. However, adapting these models to new tasks still typically requires task-specific demonstrations, action annotations, and additional fine-tuning, making deployment costly and difficult to scale. We…	Luca Franco Team	ArXiv
2026-06-05	LARA: Latent Action Representation Alignment for Vision-Language-Action Models Dexterous Manipulation VLA Visual-language action (VLA) models enable robots to predict actions directly from observations and language instructions, but their performance depends on large-scale, high-quality data and is limited by the scarcity of real-world robot action datasets. To facilitate VLA model learning with abundant unlabeled human videos, Latent Action Models (LAM) learn latent action representations from…	Siyuan Huang Team	ArXiv
2026-06-05	A Multi-Operator Mixed-Reality Interface for Multi-Robot Control and Coordination: Co-Located and Private Workspace Collaboration Dexterous Manipulation Multi-operator control of robot teams requires not only access to the same mission information, but also mechanisms for maintaining shared awareness and preventing conflicting interventions. Building on our previous HORUS interface (Holistic Operational Reality for Unified Systems) we present a mixed-reality interface that extends single-operator multi-robot supervision to collaborative…	Carmine Tommaso Recchiuto Team	ArXiv
2026-06-05	Task Editing for Generalizable 3D Visuomotor Policy Learning Dexterous Manipulation 3D visuomotor policies offer a promising direction for complex robotic manipulation, as depth maps and point clouds provide rich geometric information for spatial reasoning. However, their success often depends on large-scale real-world demonstrations, which are costly and time-consuming to collect. To this end, existing methods commonly use demonstration generation strategies to improve data…	Wei-Shi Zheng Team	ArXiv
2026-06-05	GenPO++: Generative Policy Optimization with Jacobian-free Likelihood Ratios Dexterous Manipulation Generative policies provide expressive and multimodal action distributions, making them attractive for reinforcement learning (RL) in complex continuous-control tasks. Among them, flow-based policies are especially appealing because they generate actions through deterministic transport maps. However, applying such generative policies to likelihood-based on-policy learning remains limited by the…	Ye Shi Team	ArXiv
2026-06-05	Flow of deformable droplets: self-pinned glasses and string-like flow Manipulation We investigate, through numerical simulations, the rheology of a dry suspension of deformable droplets under pressure-driven flow. The system exhibits two force-driven dynamical transitions. At low forcing, the suspension behaves as a yield-stress material: below a critical force, droplets remain arrested in an amorphous solid-like state. Our simulations suggest that yielding is controlled by…	Giuseppe Negro Team	ArXiv
2026-06-05	Hydrogel mechanics below swelling equilibrium Manipulation Hydrogels are versatile materials due to their softness and ability to undergo large changes in water content. Their mechanics, however, are complex, being a tight coupling between fluid flow and elastic deformations. We use experiments and theory to show that this coupling simplifies when hydrogels are not fully swollen. In this regime, polymer-water affinity controls local hydration, while the…	D. S. Kammer Team	ArXiv
2026-06-05	Flexible PDMS/La${0.7}$Sr${0.3}$MnO$_3$/MWCNT Composite Thin Films for Multifunctional Temperature and Magnetic Sensing Electronic Skin Manipulation The development of multifunctional electronic skin (e-skin) requires materials that combine mechanical flexibility with responsiveness to multiple stimuli. In this work, a flexible PDMS/La0.7Sr0.3MnO3 (LSMO)/MWCNT composite thin film was fabricated via solution casting, using LSMO powder synthesized by a solid-state reaction method. Structural and spectroscopic analyses confirm the formation of…	Ashutosh Kumar Team	ArXiv
2026-06-05	Beyond Waypoints: A Trajectory-Centric Waypointing Paradigm for Vision-Language Navigation Manipulation Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural-language instructions while navigating in real-world-like environments. Most VLN-CE approach-es adopt a three-stage framework: a waypoint predictor proposes navigable waypoints, and a navigator selects the best waypoint, with a low-level controller executing the movement to it. However, this…	Liqiang Nie Team	ArXiv
2026-06-05	Constraint-driven Optimization and Parametrization of Industrial NURBS Geometries via Neural Deformation Field Manipulation This work presents a differentiable framework for the parametrization and shape optimization of industrial CAD geometries represented by multi-patch NURBS surfaces. The method enables the deformation of complex CAD models through a physics-informed geometric parametrization, allowing direct morphing driven by physical constraints without the need to prescribe a predefined deformation strategy. A…	Gianluigi Rozza Team	ArXiv
2026-06-05	Enhanced viscous adhesion using deformable structure Manipulation We investigate the adhesion dynamics of a thin elastic structure in contact with a viscous fluid and retracted at a controlled speed, mimicking natural adhesion mechanisms. During detachment, the viscous fluid confined between the deformable structure and a rigid substrate generates an adhesive force due to a pressure drop within the thin film. We show from dedicated experiments that the…	Pascal Damman Team	ArXiv
2026-06-05	QuadVerse: An Integrated Framework Aligning Visual-Physical Reality for Quadruped Simulation Manipulation Sim2Real Simulation is central to robot learning, yet the sim-to-real gap remains a major bottleneck.Existing approaches often tackle visual or dynamic gaps separately, overlooking how these individual mismatches accumulate and propagate throughout the robot’s state evolution.In this paper, we introduce QuadVerse, an integrated framework that uses reconstructed scenes as a calibration substrate for…	Jin Xie Team	ArXiv
2026-06-05	Gravitational waveforms from binaries in higher-derivative gravity: a Love story Manipulation We study the emission of gravitational waves by a test particle orbiting a non-rotating black hole in higher-derivative gravity theories with cubic and quartic contractions of the Riemann tensor. To this aim, we first derive the master equations describing even- and odd-parity perturbations in the presence of an arbitrary source term, and then construct a Post-Minkowskian expansion of the…	Alejandro Ruipérez Team	ArXiv
2026-06-05	Microscopic formulation of the interacting boson-fermion model using the nuclear energy density functional Manipulation Microscopic modeling of low-energy spectroscopy in medium-heavy and heavy odd-$A$ nuclei is an outstanding open problem in nuclear physics. We propose a novel spectra-generating collective model for odd-$A$ nuclei constructed by means of the nuclear energy density functional theory and the interacting boson-fermion model. The bosonic Hamiltonian for an even-even nucleus, which is treated as a…	K. Nomura Team	ArXiv
2026-06-05	Blockchain Infrastructure for Intelligent Cyber–Physical–Social Systems:Post-Quantum Security, Interoperability, and Trustworthy Data Economies in the Era of Embodied AI Manipulation The deployment of embodied artificial intelligence via world-model-based robotics presents a transformative opportunity for blockchain infrastructure, establishing urgent demand for trustworthy data provenance, cross-organizational governance, and incentive-compatible sharing across decentralized ecosystems. Simultaneously, quantum computing advances recognized by the 2025 Nobel Prize in Physics…	Luyao Zhang Team	ArXiv
2026-06-05	ARAPDiffusion: ARAP Regularization for Diffusion-Based Deformable Shape Space Learning Manipulation This paper introduces ARAPDiffusion, a latent diffusion model to learn the underlying continuous shape space of a deformation shape collection. The key innovation is in injecting the as-rigid-as-possible (ARAP) deformation model as regularization losses into latent diffusion (LD), releasing the requirement of having abundant 3D training data for learning generative models. In contrast to the…	Qixing Huang Team	ArXiv
2026-06-05	Learning Dynamic Aperture from One-turn Maps Manipulation Dynamic aperture evaluation relies on long-term tracking, while existing machine-learning surrogates remain difficult to generalize across machines. We demonstrate that coarse-grained dynamic aperture can be learned directly from suitably encoded one-turn maps. By reformulating dynamic-aperture prediction as an image segmentation problem, a deep surrogate model captures the long-term stability…	Derong Xu	ArXiv
2026-06-04	TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies Dexterous Manipulation VLA Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations. Prior efforts to accelerate VLAs through model compression, KV-cache reuse, or reinforcement learning only shift the policy from…	Mingyu Ding Team	ArXiv
2026-06-04	VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies Dexterous Manipulation Humans often take longer to demonstrate a task than a robot would need to execute it. Rather than learning to replicate the demonstration at the same pace, many industrial and practical applications require robots to perform tasks as quickly as possible. In this paper, we investigate several hypotheses for learning policies that operate faster-than-demonstrations. Our experiments show that the…	Siddarth Jain Team	ArXiv
2026-06-04	Synthetic Data Generation and Vision-based Wrinkle and Keypoint Detection for Bimanual Cloth Manipulation Dexterous Manipulation Robotic manipulation of textiles remains challenging because continuous deformation and self-occlusions hinder the robust visual perception required to estimate the cloth’s state. To address the lack of annotated real-world data, we developed a Blender-based synthetic pipeline exporting auto-annotated keypoints, and combined manually labeled renders with real-world data to train a wrinkle…	Atal Anil Kumar Team	ArXiv
2026-06-04	Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation Dexterous Manipulation Tactile Touch sensing is beneficial for solving a wide variety of manipulation tasks. While there exists a wide range of tactile sensors with different properties, exploiting the fusion of multiple heterogeneous tactile sensors to improve manipulation learning remains underexplored. We present Multi-Resolution Tactile Sensing (MiTaS), a representation framework that leverages multiple tactile sensors…	Georgia Chalvatzaki Team	ArXiv
2026-06-04	AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understanding Dexterous Manipulation VLA Vision-Language-Action (VLA) models leverage the rich world knowledge of pretrained vision-language models (VLMs) to enable instruction-following robotic manipulation. However, the structural mismatch between VLM semantic spaces and embodied control policies often hinders the learning of precise perception–action mappings. To address this challenge, we propose \textbf{AffordanceVLA}, a unified…	Yingcong Chen Team	ArXiv / Web
2026-06-04	MotionDisco: Motion Discovery for Extreme Humanoid Loco-Manipulation Dexterous Manipulation LearnedControl We present MotionDisco, a framework that discovers contact-rich, long-horizon humanoid loco-manipulation motions from scratch, without relying on teleoperation or motion retargeting from human demonstrations. This is challenging because the space of possible contact interactions grows combinatorially with the task horizon and the number of objects in the scene. MotionDisco enables rapid discovery…	Majid Khadiv Team	ArXiv
2026-06-04	Nonreversible Gauge Fields in Fokker–Planck Dynamics: Supersymmetric Hamiltonians and Learned Finite Forces Manipulation We formulate stationary-density-preserving nonreversible perturbations of Fokker–Planck dynamics as gauge fields that deform relaxation spectra while leaving the invariant state fixed. When detailed balance holds, a similarity transformation maps the reversible Fokker–Planck operator to a Witten-Laplacian-type supersymmetric Hamiltonian; nonreversible gauges then appear as non-Hermitian…	Masayuki Ohzeki	ArXiv
2026-06-04	On the Possibility of a Strong First-Order Phase Transition in Neutron Stars Manipulation Whether cold dense QCD matter undergoes a strong first-order phase transition remains an open question. In nature, neutron stars provide the most direct probe of cold dense QCD matter. Theoretically, chiral effective field theory constrains the equation of state of dense matter near nuclear saturation density, while perturbative QCD calculations constrain it at densities well beyond stable…	Lie-Wen Chen Team	ArXiv
2026-06-04	Detecting Tidal Resonances in Binary Neutron Stars Manipulation As a binary neutron star inspirals due to the emission of gravitational waves, the rising tidal frequency resonantly excites vibrational modes. These oscillations are seismological probes of the rich stellar interior, yet it remains to be established whether gravitational-wave interferometers can measure them. Here, we present the first fully Bayesian study of the capability of the Einstein…	Chris Van Den Broeck Team	ArXiv
2026-06-04	Investigating frictional instability due to pressurization in granular media: insights from coupled computational fluid dynamics discrete element method Manipulation Fluid pressurization can reactivate subcritically stressed granular layers in faults, slopes, and injection-perturbed reservoirs, but grain-scale feedbacks among pressure diffusion, drainage, and contact-network degradation remain unresolved. Here, 3D coupled CFD-DEM simulations investigate pore-pressure-induced reactivation of confined, fluid-saturated granular shear layers under imposed shear…	Behrooz Ferdowsi Team	ArXiv
2026-06-04	Flapping instability of elastic disks in Stokes flows Manipulation Fluid-structure interactions at low Reynolds number can lead to a much richer phenomenology than previously expected. Here, we study the dynamics of a freely suspended, thin elastic disk in a shear flow, where the plane of the disk is initially parallel to the flow plane. Using a combination of experiments and simulations, we demonstrate that beyond a critical flow strength the disk deforms,…	Lorenzo Botto Team	ArXiv
2026-06-04	Small deformations of a near cylindrical tube for the Canham-Helfrich Energy with applications to biological membranes Manipulation In this article we develop a quadratic energy which approximates the Canham-Helfrich energy for a tube-like surface with clamped boundary and area constraint. The energy is suited to the study of small deformations of biological membranes where the deformations are induced by point forces or point constraints due to the cytoskeleton or a phase dependent spontaneous curvature. Since the…	Philip J. Herbert Team	ArXiv
2026-06-04	Geometry-Driven Polarization Control in Ferroelectric Nematic Liquid Crystals Manipulation Ferroelectric nematic liquid crystals (FNLCs) combine fluidity with spontaneous polarization, offering promising avenues for flexible electromechanical systems. Here, we demonstrate that mechano-electrical conversion in FNLCs can be enhanced by mechanically programming a robust macroscopic polarization alignment. Using hybrid liquid crystal cells composed of rigid glass and flexible substrates,…	Masanori Ozaki Team	ArXiv
2026-06-04	L-SDPPO: Policy Optimization of Spiking Diffusion Policy for Intra-vehicular Robotic Manipulation Manipulation Intra-vehicular robots in spacecraft help reduce astronaut workload and improve mission efficiency. Recent research focuses on using deep learning methods to achieve the acute control required for operations in these complex environments. However, objects exhibit unpredictable, unconstrained drift without gravitational damping. These factors demand robustness against complex multimodal action…	Zuoquan Zhao Team	ArXiv
2026-06-04	Sample-efficient Low-level Motion Planning for Robotic Manipulation Tasks via Zero-shot Transfer Learning Manipulation As robotic systems become more sophisticated, the growing complexity of their motion planning models and the longer training times pose substantial challenges. Evolutionary algorithms such as the Sample-efficient Cross-Entropy Method (iCEM) have recently demonstrated promising potential for low-level real-time planning by leveraging efficient knowledge reuse strategies to improve performance….	Gualtiero Colombo Team	ArXiv
2026-06-04	RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning Manipulation Tactile Learning dexterous manipulation requires demonstrations that preserve fine hand-object interactions while remaining executable at deployment. Existing pipelines either lose deployable dexterity through retargeting or embodiment conversion, or rely on robot-specific teleoperation that is costly to scale and often lacks intuitive, contact-aware control for dexterous data collection. We present…	Zongqing Lu Team	ArXiv
2026-06-04	Arithmetic Wu Formulas and the Generalized Hecke Theorem Manipulation We construct canonical Steenrod square operations on the Geisser–Schmidt/Milne modified compactly supported étale cohomology of separated finite-type schemes over rings of $S$-integers in which $2$ is invertible. This lets us extend Feng’s notion of the absolute étale Wu class from the finite-field setting to arithmetic bases away from $2$. A key technical input is a modified compactly supported…	Sa’ar Zehavi Team	ArXiv
2026-06-04	Resolving room temperature microscale fracture and plasticity of iron oxides along the cascade of iron ore reduction via nanoindentation and microcantilever bending Manipulation Understanding the fundamental mechanical behaviour of iron oxide phases is essential for controlling attrition and fracture during iron ore reduction process, particularly in hydrogen-based direct reduction systems. This study investigates the room temperature plasticity and fracture behaviour of single-crystal hematite, magnetite, and Wustite using nanoindentation and micro-cantilever fracture…	Anwesha Kanjilal Team	ArXiv
2026-06-04	Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images Manipulation Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are commonly used for this phase, each with notable limitations. Impression taking, which involves placing alginate or silicone material in a tray and inserting it into the patient’s oral cavity to form a negative mold, suffers from significant…	Sun-Young Ihm Team	ArXiv
2026-06-04	Towards a Data Flywheel for Embodied Intelligence in Logistics Manipulation Embodied intelligence is moving from laboratory demonstrations toward industrial deployment, with the logistics industry serving as a key application scenario. Learning-based policies offer a promising path beyond traditional perception-planning-control pipelines, but their scalability depends on how embodied data can be collected, organized, and reused. This research studies a data-centric…	Daqing Zhang Team	ArXiv
2026-06-02	Revisiting Embodied Chain-of-Thought for Generalizable Robot Manipulation VLA Dexterous Manipulation Embodied chain-of-thought (CoT) aims to bridge linguistic reasoning and robotic control, but its effective form and integration strategy remain underexplored. In this paper, we revisit embodied CoT for vision-language-action (VLA) models at large scale. We construct the largest embodied CoT corpus to date, comprising 978,743 trajectories, 226.3M samples, and 2592.5 hours of robot data. Through…	Huaping Liu Team	ArXiv
2026-06-02	PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models VLA Dexterous Manipulation Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation. However, deploying these models in open-ended environments requires continuously acquiring novel skills, a process that inevitably triggers severe catastrophic forgetting of previously learned behaviors. While experience replay (ER) serves as a standard mitigating strategy, naive…	Yandong Guo Team	ArXiv
2026-06-02	Grasp-Then-Plan with Failure Attribution: A Closed Two-Stage Framework for Precise and Generalizable Robotic Manipulation VLA Dexterous Manipulation In robotic manipulation, the tight coupling between grasping and motion planning often obscures the true source of failure, leading to inefficient trial-and-error. To enable efficient long-horizon manipulation, we propose GTP-FA (Grasp-Then-Plan with Failure Attribution), a task-oriented two-stage grasp-then-plan framework that generates grasp candidates and performs downstream motion planning…	Wanyuan Wang Team	ArXiv / Web
2026-06-02	SimuScene: Simulation-Ready Compositional 3D Scene Reconstruction from a Single Image Dexterous Manipulation Reconstructing interactive, simulation-ready 3D scenes from a single image is a critical bottleneck for robotic manipulation. While recent single-image lifters recover plausible per-object shapes, composing them yields scenes that collapse under physical simulation due to interpenetrating, hovering, or sinking objects. Existing physics-aware methods address this strictly as a post-hoc layout…	Hanbyul Joo Team	ArXiv / Web
2026-06-02	Preference-Calibrated Human-in-the-Loop Reinforcement Learning for Robotic Manipulation Dexterous Manipulation Human-in-the-loop reinforcement learning (HIL-RL) improves sample efficiency in real-robot manipulation through online human intervention. However, successful trajectories may include suboptimal actions that deviate from the desired task-execution path and force human intervention. Existing HIL-RL methods typically apply the consistent credit assignment principle to all transitions, uniformly…	Ziwei Wang Team	ArXiv
2026-06-02	PointAction: 3D Points as Universal Action Representations for Robot Control Dexterous Manipulation Video-Action Models (VAMs) leverage the broad visual dynamics captured by pre-trained video diffusion models, offering a promising path toward generalizable robot manipulation. However, RGB-only video rollouts are not directly actionable: they leave metric 3D motion, contact geometry, and fine-grained spatial constraints under-specified, making action grounding ambiguous. Meanwhile, scaling…	Jiatao Gu Team	ArXiv / Web
2026-06-02	Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors Dexterous Manipulation Tactile Event-based tactile sensing offers low-latency signal acquisition for contact-rich robotic interaction. This paper investigates contact-angle estimation using event streams from an event-based tactile sensor (NeuroTac) and compares three event-derived spatial contour representations: a dynamic representation capturing recent event activity, a static representation recovering a more persistent…	Benjamin Ward-Cherrier Team	ArXiv
2026-06-02	Yoctosecond imaging of the ground state of $^{129}$Xe at the Large Hadron Collider Manipulation Imaging a quantum many-body system requires probes that resolve the coordinates of its constituents in sufficiently large event samples, allowing measurements of correlation functions [1-4]. High-energy nuclear collisions provide this opportunity on the nuclear scale [5], enabling features of colliding ions, such as their deformation, to be probed through particle correlation observables [6, 7]….	Wilke van der Schee Team	ArXiv
2026-06-02	Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories Manipulation The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal…	Vahab Mirrokni Team	ArXiv
2026-06-02	Let the Dynamics Flow: Stable Flow Matching Dynamical Systems Manipulation Flow matching has recently emerged as a powerful approach for imitation learning, enabling scalable, expressive, and multimodal motion policies. However, incorporating formal stability guarantees into these generative models, a prerequisite to ensure safe and generalizable robot behaviors, remains a significant challenge. While modeling robot motions as dynamical systems allows for such…	Noémie Jaquier Team	ArXiv
2026-06-02	Optimal Design and Analytical Modeling of a Soft Fin-Ray Effect Gripper Finger Using the Finite Rigid Elements Method Manipulation Fin Ray-inspired soft grippers offer a promising solution for gently handling delicate, irregular objects, especially in agriculture. The objective of this research is to design, fabricate, and model a Fin Ray Effect (FRE) soft gripper finger to enable precise force control in future applications. This design aims to gently grasp delicate agricultural products, such as tomatoes, that require both…	Hassan Sayyaadi Team	ArXiv
2026-06-02	Nuclear Reaction Data for Fission Products Off Stability Manipulation Neutron cross sections on fission products are relevant to a wide range of applications, including nuclear nonproliferation and forensics, spent-fuel assay, reactor burnup and design, as well as astrophysics. Evaluated nuclear data libraries generally fulfill application needs for isotopes on or near stability, however, for unstable fission products, theoretical descriptions of neutron-induced…	Shusen Liu Team	ArXiv
2026-06-02	A Plunge into the Chasm: Surviving Tidal Effects in Kerr Spacetime Manipulation We investigate the fate of an observer falling towards a Kerr black hole. The tidal forces are computed for arbitrary trajectories of an observer, and we specify them along the polar axis in order to remain as far as possible from the ring-shaped singularity. Our analysis shows that an observer is not tidally disrupted during the fall provided that the black hole mass exceeds a critical value,…	Claude Semay Team	ArXiv
2026-06-02	NVIDIA Isaac Sim: Enabling Scalable, GPU-Accelerated Simulation for Robotics Manipulation Simulation has become a core infrastructure for robotics research. Unlike previous simulators, NVIDIA Isaac Sim leverages GPU acceleration to enable large-scale parallel training and physics-accurate modeling. Its synthetic data generation pipeline alleviates the scarcity of high-quality training data, supporting data-driven robot learning and large-scale simulation-centric experimentation….	Yang Song Team	ArXiv
2026-06-02	SPADE: Sketch-guided Path Planning Augmented with Diffusion Experts Manipulation Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations. However, these approaches face two key…	Anthony Rizk Team	ArXiv
2026-06-02	Renormalization aspects of the Yang-Mills theory with a cutoff Manipulation The paper discusses renormalization aspects of the quantum four-dimensional Yang-Mills theory with a cutoff regularization in the coordinate representation. The background field method is used to formulate a generating functional, and the regularization is introduced through quasi-local probabilistic averaging. Two main types of regularization are proposed: strong deformation, which consists in…	N. V. Kharuk Team	ArXiv / Web
2026-06-02	Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots Manipulation Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction. However, it remains challenging because of substantial morphological discrepancies between humans and robots, including differences in skeletal topology, limb proportions and degrees of freedom, as well as the scarcity of paired motion data. This paper presents…	Shiwu Zhang Team	ArXiv / Web
2026-06-02	Magnetic field effects on spin-split band and magnon transport in altermagnets and emergent compensated ferrimagnets Manipulation In altermagnets and fully compensated ferrimagnets, not only the electron band but also the magnon band exhibits spin splitting without net magnetization, which enables thermal activation of the magnon spin current. Here, we theoretically investigate magnetic field effects on the magnon properties of these antiferromagnets in the presence of a weak easy-axis anisotropy which makes the collinear…	Hikaru Kawamura Team	ArXiv
2026-06-02	GPU-Parallel Multi-Task Reinforcement Learning with Demonstration Guided Policy Optimization Manipulation Sim2Real Large scale GPU-parallel reinforcement learning has changed what can be trained in robot simulation, yet most systems still optimize one specialist policy per task. We propose a construction methodology for turning structured manipulation task families into GPU-parallel multi-task RL benchmarks, and instantiate it as MT-Libero using LIBERO assets and task predicates in Isaac Lab. The resulting…	Weihua Zhang Team	ArXiv
2026-06-02	EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations Manipulation Dexterous manipulation learning has long been hindered by the high costs of data and training, as pure reinforcement learning typically requires large-scale interactive exploration and imitation learning depends on high-quality demonstrations that are expensive to collect. To address this problem, we propose EaDex, a multi-embodiment dexterous manipulation learning framework under low-cost…	Yingtian Li Team	ArXiv