Robotics
Position: Vision-Language-Action Models Cannot Be Verified to Perform Physical Reasoning
Taozhao Chen, Ian Manchester and Huaming Chen
Vision-
Language-Action (VLA) systems, built on pretrained
vision-
language models (VLMs), have shown rapidly improving performance on
robot manipulation
benchmarks. These gains are commonly interpreted as evidence that semantic representations learned from internet-scale data transfer to physical execution generalization. This position paper argues that the assumption underlying this interpretation – that semantic generalization is sufficient to support physical action decisions – has not been independently verified and cannot be tested under current evaluation protocols. We support this claim by decomposing VLA policies into semantic mapping and physical action decision, and showing that task success rate – the dominant evaluation metric – cannot distinguish between these two sources of capability. As a result, improvements in
benchmark performance are consistent with multiple competing explanations, including semantic matching, distributional overlap, and genuine physical generalization. We further argue that this identifiability gap has been reinforced through narrative drift, whereby successive systems inherit and strengthen prior interpretations of performance gains without isolating the underlying causal mechanism. To address this limitation, we propose a research direction based on evaluation designs that introduce controlled variation to separately measure semantic and physical generalization. Such designs make it possible to causally attribute performance without requiring access to model internals, and to empirically assess the role of VLM backbones as semantic interfaces rather than implicit sources of physical competence. Our goal is not to refute the role of VLMs in robotics, but to clarify the conditions under which claims of physical generalization can be meaningfully evaluated.
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Robotics
Hierarchical Policy Learning via Spectral Decomposition
Shuxin Cao, Liquan Wang, Walker Byrnes et al. (+3 more)
In this paper, we identify a semantic decomposition in
robot action sequences, separating task-level motion intent from execution-level refinements. By analyzing actions in the spectral domain using the discrete cosine transform (DCT), we observe that low-frequency components capture global motion trajectories, while high-frequency components encode precise timing,
alignment, and contact behaviors. Motivated by this structure, we propose Causal Spectral Policy (CSP), which models action generation as a causal coarse-to-fine process: coarse motion is predicted from observation and
language, and fine corrections are generated conditionally on the realized trajectory. Across simulation and real-world evaluations, CSP consistently outperforms strong baselines on precision-sensitive manipulation tasks. Additionally, we propose human-inspired teleoperation noise injection as a data augmentation method, under which our approach demonstrates strong robustness to noisy demonstrations.
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All authors Shuxin Cao, Liquan Wang, Walker Byrnes, Yiye Chen, Yilun Du, Animesh Garg
Robotics
Analyzing Uncertainty in the Spatial Representation of the Kinematic Bicycle Model
Shafayat Abrar, M. Zaeem Baig, Shahir Ul Islam Anzal et al. (+1 more)
Locating a vehicle and determining its orientation in an uncertain environment is a critical challenge in autonomous vehicle
navigation and path planning. To address these challenges, a vehicle estimates its pose while depending on sensor data that offer noisy measurements. These uncertainties in pose quantities are expressed mathematically as a covariance
matrix. The real-time computation of the covariance
matrix is critical because of the non-linearity involved in the kinematic model. The challenge is thus to evaluate the
evolution of the covariance
matrix of a vehicle's discretized
stochastic kinematics. The purpose of this study is to obtain a near-accurate
evolution of the covariance
matrix of the rear-wheel bicycle kinematic model under uncertainties in wheel displacement and steering angle. We used Taylor's series to linearize the nonlinear trigonometric functions and provided closed-form expectations of random variables with the required accuracy. Our analytical findings are in good agreement with those obtained from Monte-Carlo simulations. Our contribution is probably the first detailed closed-form presentation of the covariance
matrix constituents of the vehicle under evaluation, which were previously reported either incorrectly or incompletely. These findings aid in identifying the potential and constraints of the discretized kinematic model as well as its
stochastic analysis. The techniques presented here are useful for the simultaneous localization and odometry self-calibration of certain mobile
robots and autonomous vehicles.
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All authors Shafayat Abrar, M. Zaeem Baig, Shahir Ul Islam Anzal, Abdul Basit Memon
Robotics · Cross-listed from cs.LG
VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction
Chuheng Wei, Ziye Qin, Ziran Wang et al. (+1 more)
Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within limited time and distance margins. Accurate prediction of both stop-go decisions and decision timing is important for adaptive signal control, advanced driver assistance systems, and human-centered intelligent transportation applications. However, dilemma zone behavior is strongly driver dependent. Similar approach trajectories may lead to different decisions across drivers because of differences in risk preference, braking habit, and decision threshold. Existing personalized models often rely on handcrafted scalar descriptors, which provide useful but limited summaries of individual behavior. This paper proposes VISTA-DZ, a semantic-profile-conditioned framework for personalized stop-go and decision-time prediction. Historical trajectories are converted into visual representations, interpreted by a
vision-
language model to generate behavioral profiles, and encoded as semantic
embeddings to condition a dual-output prediction network. The final model combines a bidirectional GRU encoder, driver-conditioned multi-head cross-
attention, and Feature-wise Linear Modulation for temporal evidence selection and feature adaptation. Experiments on the SDZ
dataset and a newly collected FDZ
dataset show that VISTA-DZ outperforms trajectory-only and handcrafted personalization baselines, achieving 93.26% in-domain simulation accuracy and 90.22% mean accuracy across 20 held-out simulation drivers. Cross-domain results further show feasible zero-shot simulation-to-real transfer and better real-world generalization when simulation data are combined with limited field data.
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All authors Chuheng Wei, Ziye Qin, Ziran Wang, Guoyuan Wu
Robotics
CORE: Common Outcome Regularities from Action-Free Visual Demonstrations for Robot Manipulation
Juyi Sheng, Mingxin Tan, Jincheng Li et al. (+1 more)
Robot imitation learning often relies on costly
robot demonstrations, while abundant action-free visual demonstrations, such as human
videos, are difficult to use because they lack
robot-executable actions and suffer from embodiment gaps. We propose CORE, a policy learning framework that extracts Common Outcome Regularities (CORE) from visual demonstrations. Rather than transferring explicit actions across embodiments, CORE exploits a key observation: although successful trajectories for the same task can be diverse, their terminal states often share stable object configurations, spatial relations, and contact constraints. CORE first trains a terminal outcome encoder with contrastive and auxiliary temporal objectives, then aggregates successful terminal
embeddings into visual goal prototypes, and finally injects these prototypes as global goal conditions into
robot policies. Compared with
language instructions, visual goal prototypes provide more concrete
geometric and physical constraints for task completion. Across Meta-World, RoboTwin 2.0, and real-world manipulation, CORE improves the average success rate of the corresponding policy backbones by up to +3.9, +11.1, and +17.0 percentage points, respectively, and outperforms text-conditioned variants under the evaluated settings. The project and code are available at https://logssim.github.io/CORE.github.io/.
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All authors Juyi Sheng, Mingxin Tan, Jincheng Li, Mengyuan Liu
Robotics
Learning Transferable Dynamics Priors from Action to World Modeling
Ze Huang, Jiahui Zhang, Hairuo Liu et al. (+3 more)
We study action-conditioned world modeling as a scalable way to learn transferable dynamics priors for
robot learning. By pretraining a model to predict how actions drive visual scene
evolution, the resulting world model captures reusable interaction dynamics beyond appearance-level
video generation. Concretely, we pretrain a multi-view interactive base
diffusion world model, A2World, on large-scale
robot manipulation data with real action annotations. We validate the learned dynamics priors from two complementary perspectives. First, we adapt A2World into a task- or scene-specialized real-world simulator, A2World-sim, whose long-horizon rollouts support simulator-based policy evaluation and scalable what-if analysis by replacing real-
robot rollouts with world model rollouts. Second, starting from the same pretrained weights, we adapt A2World into a
video-action joint prediction model, A2World-policy, that predicts actions under visual and instruction conditioning. Experiments across simulation
benchmarks and real-
robot settings demonstrate that action-conditioned world model pretraining yields transferable dynamics priors that benefit both simulator-centric and policy-centric
robot learning.
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All authors Ze Huang, Jiahui Zhang, Hairuo Liu, Chenxi Zhang, Ran Cheng, Li Zhang
Robotics
MTD-Map: Single-Stage Long-Term LiDAR Map Maintenance Framework via Mixture Transition Distribution
TaeYoung Kim, Gilhwan Kang, Tae Ihn Kim et al. (+2 more)
While robust map maintenance has advanced significantly, existing studies have focused on specific tasks, especially dynamic object removal or change detection. In this paper, we take a holistic view of the map maintenance problem and propose MTD-Map, a single-stage framework that handles both dynamic object removal and change detection without separate task-specific modules. MTD-Map employs an explicit representation that compactly encodes the direction and duration of occupancy transitions through Mixture Transition Distribution (MTD) modeling. We develop a recursive MTD formulation that encodes historical occupancy patterns into an augmented state to capture high-order temporal dependencies. Furthermore, a stability-driven adaptive strategy balances noise suppression with the preservation of quasi-static structures. Extensive experiments verify that MTD-Map robustly removes dynamic objects and achieves competitive change detection performance, subsequently reducing computational costs. Our project page is available at: https://taeyoung96.github.io/mtd_map/.
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All authors TaeYoung Kim, Gilhwan Kang, Tae Ihn Kim, Seungwon Song, Hun Keon Ko
Robotics
Understanding LLM Intervention Explanations in Multi-Party Human-Robot Interaction
Micol Spitale, Massimiliano Nigro and Emily Cross
Large
Language Models (LLMs) are increasingly embedded in social
robots to support natural group interactions, yet their role in complex multi-party settings remains underexplored. In particular, it is unclear how LLM-driven
robots decide when and why to intervene in group conversations. This paper investigates the intervention explanations generated by an LLM-based orchestrator in a multi-party interaction involving three human participants and two
robots. We conducted a between-subjects study with 24 groups (66 university students), comparing a homogeneous condition (two
robots with the same role, i.e., a mover) and a heterogeneous condition (two
robots with different roles, i.e., a mover and an opposer). At each conversational turn, the LLM orchestrator decided whether to intervene and generated a textual explanation of its decision. We performed a thematic analysis of 610 intervention explanations, identifying five recurring themes. Results show that explanations are facilitation-oriented, emphasizing agreement, participation, and interaction flow. While patterns remain stable across conditions, role differentiation emerges: the mover supports coordination, whereas the opposer drives goal-oriented interventions. These findings contribute to explainable AI by characterizing how LLM-driven systems justify intervention decisions in real-time, multi-party human-
robot interaction.
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Robotics · Cross-listed from cs.CV
Event-VLA: Action-Conditioned Event Fusion for Robust Vision-Language-Action Model
Jiaxin Liu, Xun Xu, Zhenhao Zhang et al. (+5 more)
Vision-
Language-Action (VLA) models have become an important paradigm of
embodied AI. However, existing VLA models typically assume well-lit and stable indoor settings, while real-world
embodied manipulation may involve degraded RGB observations caused by illumination shifts, posing critical challenges for robust robotic manipulation. To address this gap, we propose Event-VLA, an event-enhanced VLA framework for generalizable manipulation across varying illumination conditions. We formulate VLA-based manipulation under degraded visibility as a practical robustness problem for RGB-centric policies, and introduce event streams as an illumination-robust, motion-sensitive complementary observation to improve robustness across visibility levels. Specifically, unlike conventional
multimodal fusion that directly merges event features into the global semantic
token space, Event-VLA injects event information through an action-query routing pathway. It uses learnable action queries to extract task-relevant semantics from the VLA
reasoning process, and selectively aggregates event
tokens via gated cross-
attention to construct event-aware action representations. This design preserves the pretrained RGB-
language semantic priors while effectively leveraging event information for robust action prediction. Experiments in simulation and real-world deployment show that Event-VLA maintains strong manipulation performance under normal lighting and improves success rates under low-light degradation and near-dark real-world settings.
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All authors Jiaxin Liu, Xun Xu, Zhenhao Zhang, Hanqing Wang, Ruiqi Chen, Shi Chang, Weiyu Guo, Laurent Kneip
Robotics
SPACE: Swarm Pheromone Fields for Adaptive Collision-Aware Exploration
Haohua Que, Haojia Gao, Mingkai Liu et al. (+3 more)
Massive
robot swarms can explore unknown environments quickly, but adding
robots eventually stops helping. Doorways and dense traffic create congestion, increasing inter-
robot contacts and reducing the value of each additional
robot. We study this safety-efficiency tradeoff for ground swarms of tens to hundreds of
robots. We present SPACE, Swarm Pheromone Fields for Adaptive Collision-Aware Exploration. Inspired by ant foraging, SPACE maintains a shared environmental field with an attractive frontier pheromone, a repellent explore pheromone, and a fast
robot-density field.
Robots use local field guidance on a synchronized shared map. We evaluate SPACE on real building floorplans, namely sixteen home layouts from the HouseExpo
dataset and eight campus floors from the KTH
dataset, with swarms of up to two hundred and fifty-six
robots. At congested swarm sizes, SPACE attains the lowest inter-
robot contact rate on HouseExpo and lies on the empirical Pareto frontier on KTH, together with handed lane formation at sizes where Lane has fewer contacts. Relative to a greedy nearest-frontier planner, SPACE reduces the contact rate by four to seventeen times while keeping coverage time within about two percent. The results indicate that, at this scale, coordination mainly improves safety rather than coverage time.
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All authors Haohua Que, Haojia Gao, Mingkai Liu, Qian Zhang, Jiajun Sun, Fei Qiao
Robotics
LAMP: Long-Horizon Adaptive Manipulation Planning for Multi-Robot Collaboration in Cluttered Space
Shuai Zhou, Yorai Shaoul and Jiaoyang Li
Multi-
robot manipulation requires jointly
reasoning about contact formations,
robot motions under coupled dynamics, and collision avoidance. Systematically searching over this large space is difficult and becomes increasingly intractable as the number of
robots grows, the task horizon lengthens, or the scene becomes more densely cluttered. Existing approaches therefore either learn to solve the problem end-to-end via reinforcement learning or restrict planning to a simpler surrogate problem, such as planning object motions while learning short-horizon contact primitives. However, neither paradigm scales to the problem instances we target: long-horizon multi-
robot manipulation in extremely dense environments. In this paper, we propose Long-horizon Adaptive Manipulation Planning (LAMP), a framework combining a generative model for manipulation with classical planning for long-horizon
reasoning. We instantiate our framework with two
algorithms leveraging insights from established planning techniques, A* and lazy search: LAMP-A*, which systematically searches over the coupled object-
robot space, and LAMP-Lazy, a lazy planner that enables real-time replanning through deferred evaluation. Experiments in challenging simulated environments demonstrate that our approach solves complex long-horizon tasks in highly cluttered environments that prior methods cannot handle.
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Robotics · Cross-listed from eess.SY
Robust Extended Kalman Filter for Land Navigation Using Massive Array of MEMS IMUs
Omer Hanani and Alon Kipnis
We propose a robust Extended Kalman Filter (EKF) architecture for land
navigation using an array of hundreds of low-cost micro-electromechanical systems (MEMS) inertial sensors. The main challenges in this setting are bursty sensor-specific bias errors, bias drift, and the need to aggregate many inertial measurements without increasing the computational burden of the
navigation filter. To address these challenges, we introduce Robust Inertial Sensor Array Fusion (RISAF), a pre-filtering framework that combines dynamic percentile gating with real-time bias tracking before the EKF prediction step. The proposed aggregation suppresses anomalous sensor readings and compensates for individual sensor drift while preserving the vehicle-level kinematic signal. Because the resulting fused inertial measurements are passed to a standard EKF, the
navigation filter retains a minimal state vector and supports real-time execution. We evaluate RISAF through extensive simulations and real-world field tests in GNSS-denied environments, with the data provided as supplementary material. Compared with a baseline that averages the sensor readings, RISAF achieves substantially improved azimuth accuracy and reduced drift accumulation. These results demonstrate that robust fusion of large MEMS inertial arrays can bridge a substantial part of the gap between cost-effective hardware and tactical-grade inertial
navigation performance.
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Robotics
PL-LIT: A LiDAR-Inertial-Thermal SLAM Using Point-Line Features and Thermographic Mapping
Jiawei Xia, Yixiao Feng, Yongliang Shi et al. (+4 more)
Thermal imaging is resilient to adverse conditions, such as intense illumination, low-light operation, and fog, and can therefore mitigate odometry degradation when visible-spectrum imagery becomes unreliable. Nevertheless, most thermal cameras employ automatic gain control (AGC), and thermal images often present low global contrast despite containing informative edge structures. These characteristics undermine brightness constancy and cause conventional optical flow tracking-based odometry pipelines that fundamentally rely on the brightness constancy assumption across consecutive frames. To address these issues, we propose a general
LiDAR-Inertial-Thermal SLAM system that accommodates both visible-light and thermal cameras. PL-LIT combines an online photometric calibration module with a deep neural network for point-line feature extraction, enabling more stable and repeatable thermal tracking. For state estimation, we design a tightly coupled
LiDAR-Inertial-Thermal formulation within an Error-State Iterated Kalman Filter (ESIKF). We further introduce a line-feature constraint scheme ensuring the reliability of
geometric constraints across varying thermal appearances. In addition, PL-LIT builds a probabilistic thermal-intensity voxel map, which supports real-time thermal anomaly detection. Extensive experiments demonstrate that PL-LIT exhibits generality and robustness in visible-light environments, achieves state-of-the-art performance on long-range thermal infrared
datasets, and provides practical safety inspection functionality based on thermographic mapping.
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All authors Jiawei Xia, Yixiao Feng, Yongliang Shi, Chao Gao, Renjing Xu, Weining Lu, Bin Liang
Robotics
MoPe: Motion Permanence for Robust Monocular Gaussian Mapping in Dynamic Environments
Qixin Xiao
Robust
robot autonomy depends on scene representations that remain stable enough to support localization,
navigation, and downstream decision making in dynamic environments.
Monocular Gaussian Splatting SLAM provides high-fidelity mapping, but current uncertainty-aware methods still treat dynamic regions largely as per-frame observations. This makes the representation effectively memoryless: when a pedestrian slows, pauses, or reappears after occlusion, the current frame may look static, allowing dynamic content to be absorbed into the map and leaving persistent ghosting artifacts. We argue that this failure reflects a representation-level mismatch. Dynamic-ness is not an instantaneous appearance property, but a temporal property defined by motion history. Building on this view, we introduce Motion Permanence: the principle that an object's dynamic identity should persist over time rather than be re-decided from each frame independently. We realize this principle in MoPe, a
memory-aware uncertainty filter for
monocular Gaussian mapping. MoPe propagates the historical dynamic posterior through geometry-consistent SE(3) warping and fuses it with current-frame evidence using bounded
Bayesian log-odds updates. The resulting persistent posterior guides tracking, mapping, dynamic-aware Gaussian insertion, and Gaussian-level post-cleanup. On Wild-SLAM, Bonn, and TUM sequences, MoPe improves tracking robustness and reduces residual ghosting, with the strongest gains on dynamic-human scenes that most directly violate the memoryless assumption. These results show that maintaining temporal dynamic state inside the scene representation is a practical step toward more reliable representation-centric autonomy in changing real-world environments.
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Robotics
CORE Planner: Contextual-memory Oriented Reinforcement-learning in Unknown Environments for Robot Navigation
Jintao Kong, Zhihao Zhang, Weihuang Chen et al. (+4 more)
Autonomous
navigation in unknown environments requires a
robot to efficiently reach a predefined goal while exploring without prior maps. Although progress has been made in this area, most existing works still rely on traditional planning methods with hand-crafted rules, while learning-based methods often suffer from limited environmental
memory and challenges in simulation-to-real (sim-to-real) transfer. To overcome these limitations, we propose a Contextual-
memory Oriented Reinforcement-learning (CORE) planner for
robot navigation in unknown environments. The proposed CORE planner effectively combines the core advantages of traditional and learning-based methods. Specifically, our method uses a sparse visibility graph for structured environment representation, reducing the computational overhead of dense grid maps, and employs a
Transformer network to achieve a holistic environmental understanding, thereby significantly improving
navigation efficiency. Moreover, we introduce a visibility graph-based graph sparsification method and a contextual
memory mechanism, which alleviates local optima and enhances computational performance in large-scale scenes. Finally, our approach achieves zero-shot sim-to-real transfer after training solely on image-based environments, requiring no fine-tuning. Experimental results show that CORE Planner consistently outperforms state-of-the-art methods, including the traditional FAR Planner and all learning-based baselines, across representative environments, reducing travel distance by 13% over traditional FAR Planner and by up to 48% relative to learning-based baselines, with larger gains observed in more complex environments. In real-world scenarios, CORE successfully navigates without human intervention, showcasing zero-shot sim-to-real transfer. Code is available at https://github.com/BBD00/core_planner.
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All authors Jintao Kong, Zhihao Zhang, Weihuang Chen, Liming Chen, Zhongyu Guo, Shuaiyu Liu, Hongbin Sun
Robotics
AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance
Shuning Li, Sikai Li, Jiachen Li et al. (+1 more)
We present AnyBody, a unified whole-body
humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time. Prior physics-based trackers either rely on expensive full-body motion capture and error-prone trajectory retargeting, which bottleneck scalable data collection and policy learning, or decompose upper- and lower-body control into separate hierarchical representations, sacrificing the coordinated whole-body motions that loco-manipulation requires. We close this gap by learning a single latent motion representation that any keypoint subset can address. To achieve this, we first train a privileged teacher tracker on a large unstructured motion corpus and distill it online into a deterministic encoder-decoder student whose latent space is a unit sphere. We then train a
transformer keypoint encoder that admits any subset of body keypoints through masked self-
attention, aligning it to the privileged latent. Additionally, we treat the frozen decoder as a motor prior and specialize downstream tasks with a lightweight residual corrector in the latent space. We demonstrate the effectiveness of AnyBody by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including locomotion, in-air writing, and obstacle-reach.
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All authors Shuning Li, Sikai Li, Jiachen Li, Mingyu Ding
Robotics
Behavior Uncloning: Distilling Mode Redirection into Policy Weights without Inference-Time Steering
Hao Wang, Jiuzhou Lei, Dayou Li et al. (+5 more)
Behavior-cloned policies often learn multiple behavior modes from demonstration
datasets, including modes that are unsafe or otherwise undesired at deployment. For example, a policy trained on diverse handover demonstrations may learn to pass a knife blade-first. Standard remedies such as data curation and inference-time steering either require access to the original demonstrations for full retraining or add substantial inference-time overhead. To address this gap, we propose MoRE(Mode Redirection), which redirects policy rollouts toward desired behavior modes through a short "uncloning" step. Specifically, MoRE distills the redirection signal from a temporary mode classifier into the policy weights to steer behavior. A retain loss balances this edit by preserving desired-mode competence, allowing the standalone policy to suppress unwanted modes with zero inference-time overhead. Across eight simulated and real-world tasks, MoRE improves the average deployment success rate (SR) by 44 percentage points over the original mixed-mode policy. Among all compared adaptation and steering baselines, MoRE achieves the strongest SR and approaches the filtered-data retraining reference, while preserving task competence and inference speed. MoRE also generalizes across
robot policy backbones, including
Diffusion Policy and the Pi0.5 VLA, diverse task categories, and real-world deployments.
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All authors Hao Wang, Jiuzhou Lei, Dayou Li, Bangya Liu, Minghui Zheng, Manling Li, Ruohan Zhang, Zhiwen Fan
Robotics
Empowering a Single-Frequency GNSS Receiver to Achieve High-Precision Positioning with Relative Observations
Xingpeng Wang, Ziwen Qu, Juncheng Chen et al. (+8 more)
Global
Navigation Satellite System (GNSS)
navigation is widely used to provide absolute, outdoor positioning in field robotics. Advances in Real-Time Kinematic (RTK) technology can achieve centimeter-level accuracy, facilitating autonomous
navigation tasks. However, the cost and extra infrastructure used for RTK still hinder the application and more cost-effective solutions are desired. In this letter, we present a novel tightly-coupled state estimation framework that achieves high-precision localization by using low-cost, mass-
market single-frequency GNSS receivers with any relative motion sensors (e.g., wheel encoder, camera,
LiDAR). We propose a sliding-window factor graph that integrates generic relative motion with global epoch-to-anchor constraints derived from continuous carrier phase tracking. To eliminate the reliance on physical base stations, we introduce a virtual anchor mechanism: upon the initial observation of a satellite, its state is locked as a virtual reference to establish global epoch-to-anchor constraints. By substituting multi-frequency hardware redundancy with single-frequency multi-modal kinematic priors and a robust cycle-slip recovery technique, our approach ensures carrier-phase integrity on cheap receivers. Extensive real-world experiments on heterogeneous low-cost sensor suites validate that our method improves the accuracy of a single-frequency receiver from several meters to decimeter-level precision across diverse environments, providing an accurate, cost-effective and reliable alternative for autonomous
navigation.
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All authors Xingpeng Wang, Ziwen Qu, Juncheng Chen, Ruitian Pang, Xiangyu Li, Tiancheng Lai, Siqi Shen, Wentao Liu, Pengfei Wang, Chao Xu, Yanjun Cao
Robotics
TacGen: Touch Is a Necessary Dimension of Physical-World Representation – Addressing Tactile Data Scarcity with Scalable Vision-to-Touch Alignment and Generation
Wanghao Ye, Aarosh Das, Sihan Chen et al. (+19 more)
Touch resolves the physical-property ambiguity left by
vision: exploratory contact recovers shape, texture, compliance, and material, and visuo-haptic object representations converge in ventral visual cortex. We ask whether representation learning can reproduce this grounding. TacGen mitigates the tactile-data scarcity bottleneck by combining pre-specified V+T contrastive
alignment with a latent-space residual-MLP V->T generator that synthesizes tactile latents from RGB for tactile-data scaling. With matched DINOv2 backbones, splits, and probes, V+T improves matched V-only on mass (Delta R^2=+0.570), density (Delta acc=+0.067), hardness (+0.117), and uncertainty-banded force labels (Delta R^2=+0.281); all CIs exclude zero. The same representation lifts matched-capacity TACTO manipulation 0.246->0.979 while V-only capacity scaling accounts for only 4.5% of the gap, preserving 95.5%. The generator reaches cross-seed +0.589, with real tactile +0.585 inside the seed interval; the architecture comparison shows a 13pp downstream gap between reconstruction quality and representation utility. Across five-seed SSVTP/TVL reproductions, YCB-Sight transfer, three-backbone checks, permutation/random-feature controls, hash-verified manifests, and measured-force validation checks, the evidence supports the claim that touch supplies a necessary physical evidence channel for representations of contact-dependent properties.
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All authors Wanghao Ye, Aarosh Das, Sihan Chen, Yiting Wang, Bowei Tian, Guoheng Sun, Shwai He, Zheyu Shen, Ziyao Wang, Yexiao He, Zhaoyi Liu, Meng Liu, Yuning Zhang, Meng Feng, Ziyi Wang, Yilong Dai, Yifei Dong, Siyuan Peng, Zhenle Duan, Joshua Liu, Lang Xiong, Ang Li
Robotics
Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs
Aditya Prakash, Patricio A. Vela and Panagiotis Tsiotras
Continuum
robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external contact forces acting anywhere along the
robot body. Estimating these forces at unknown locations is an ill-conditioned problem, particularly for multiple contacts. We propose a unified shape and force estimation framework formulated on a factor graph. By incorporating a Gaussian mixture force parameterization into a discretized probabilistic Cosserat rod model, we reduce the dimensionality of the unknown external forces and mitigate the ill-conditioning of node-wise force estimation. The framework fuses strain, tendon tension, and pose measurements to simultaneously estimate the
robot's shape and external forces while accounting for modeling and sensor uncertainties. Numerical simulations demonstrate that the proposed method outperforms existing methods in terms of force location and magnitude estimation for both single and multi-contact scenarios. We further present a progressive variant that introduces basis functions on demand to estimate contact forces sequentially during a simulated confined-
navigation task.
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Robotics
On the Identifiability of Aided Inertial Navigation Under Measurement Delays: A Geometric Approach
Jonathan Kelly
In aided inertial
navigation, measurements from different sensors are often subject to unknown relative time delays. Consider a single aiding sensor whose measurements have an unknown but constant delay relative to the inertial-measurement data stream. We study the identifiability of the delay and the initial
navigation state parameterizing the trajectory. Identifiability depends on both the temporal structure of the aiding measurements and the form of the trajectory. Using the special Galilean group, we determine the minimal number and type of aiding measurements needed to recover the delay and the
navigation state. We also characterize a class of uninformative trajectories, for which the delayed measurement model admits a continuous
symmetry that prevents unique delay-and-state recovery. We show that each such trajectory is generated by a constant element of the Galilean Lie
algebra, and connect this result to the familiar linearized, Jacobian-based analysis.
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