News & Research

arXiv:2512.10128v4 Announce Type: replace Abstract: Spatially inhomogeneous magnetic fields offer a valuable, non-visual information source for positioning. Among systems leveraging this, magnetic field-based simultaneous localization and mapping (SLAM) systems are particularly attractive. These systems execute positioning and magnetic field mapping tasks simultaneously, and they have bounded positioning error within previously visited regions. However, state-of-the-art magnetic-field SLAM methods typically require low-drift odometry data provided by visual odometry, a wheel encoder, or pedestrian dead-reckoning technology, which may not be available under certain situations. To address this limitation, this work proposes two magnetic inertial SLAM (MI-SLAM) systems --- a loosely coupled version and a tightly coupled version --- that rely solely on low-cost sensors: an inertial measurement unit (IMU) and seven magnetometers. Both systems use a magnetic field aided inertial navigation s

arXiv:2510.24069v2 Announce Type: replace Abstract: To generate reliable motion for legged robots through trajectory optimization, it is crucial to simultaneously compute the robot's path and contact sequence, as well as accurately consider the dynamics in the problem formulation. In this paper, we present a phase-based trajectory optimization that ensures the feasibility of translational dynamics and friction cone constraints throughout the entire trajectory. Specifically, our approach leverages the superposition properties of linear differential equations to decouple the translational dynamics for each contact point, which operates under different phase sequences. Furthermore, we utilize the differentiation matrix of B{\'e}zier polynomials to derive an analytical relationship between the robot's position and force, thereby ensuring the consistent satisfaction of translational dynamics. Additionally, by exploiting the convex closure property of B{\'e}zier polynomials, our method ensur

arXiv:2510.04724v2 Announce Type: replace Abstract: This paper introduces a methodology for task-specific design optimization of multirotor Micro Aerial Vehicles. By leveraging reinforcement learning, Bayesian optimization, and covariance matrix adaptation evolution strategy, we optimize aerial robot designs guided exclusively by their closed-loop performance in a considered task. Our approach systematically explores the design space of motor pose configurations while ensuring manufacturability constraints and minimal aerodynamic interference. Results demonstrate that optimized designs achieve superior performance compared to conventional multirotor configurations in agile waypoint navigation tasks, including against fully actuated designs from the literature. We build and test one of the optimized designs in the real world to validate the sim2real transferability of our approach.

arXiv:2507.04167v2 Announce Type: replace Abstract: Rising labor costs and narrow treatment windows have made teleoperated robots a proposed tool for vineyard scouting and treatment, but simulation-based comparisons of human and robotic performance are sensitive to modeling choices that can favor one agent before any trial runs: whether both can record a target's location, whether both are routed efficiently, and whether travel speeds are assessed consistently. This paper simulates a human operator against a teleoperated robot on two vineyard tasks, disease detection-and-treatment and yield-zone survey, holding both agents to matched location-marking and routing standards, using a routing strategy proven optimal for this topology, and modeling teleoperation-specific effects. Once information access and routing are held equal, task-time outcomes are governed largely by relative sustained travel speed: at the speeds modeled (1.4 m/s human, 0.9 m/s UGV), the human completes both tasks 43-

arXiv:2503.13048v4 Announce Type: replace Abstract: Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EIT-based tactile sensors face significant challenges during surface bending. Deformation alters the baseline impedance distribution and couples with touch-induced conductivity variations, complicating signal interpretation. To address this challenge, we present a novel sensing framework that integrates a deep neural network for interaction state classification with a dynamic adaptive reference strategy to decouple touch and deformation signals, while a data-driven regression model translates EIT voltage changes into continuous bending angles. The framework is validated using a magnetic hydrogel composite sensor that conforms to bendable surfaces. Experimental evaluations demonstrate that the proposed framework achieves precise and robust bending angle estimat

arXiv:2608.25690v1 Announce Type: cross Abstract: Sequential decision-making in multi-robot systems typically assumes that planning information is reliable and that agents execute the actions anticipated by the planner. Compromised agents can violate both assumptions, creating a mismatch between the planning model and physical execution. We study this problem in online multi-robot routing under localization spoofing. We introduce a distance-constrained spoofing model for monitor-aware adversaries, together with a tiered bipartite matching strategy that maximizes assignment influence while limiting spoofing magnitude. To mitigate such attacks, we develop a trust-aware monitor that combines probabilistic localization trust, calibrated using real GPS spoofing data, with behavioral evidence from task execution to classify agents and remove detected adversaries from subsequent planning. We further show that undetected adversaries can cause rollout to lose its expected cost-improvement behav

arXiv:2608.25435v1 Announce Type: cross Abstract: Fine-grained segmentation of communication-tower components in UAV imagery is essential for automated inspection, yet task-specific models are hard to develop due to limited instance-level annotations. Zero-shot segmentation models offer a promising alternative, but in cluttered scenes, visually similar background structures interfere with component localization, causing missed instances and false positives. We propose a model-agnostic saliency-depth foreground-conditioning strategy combining appearance-based saliency with monocular relative depth to construct a coarse tower prior and suppress irrelevant content. We integrate this module with Grounded-SAM and SAM 3, yielding SD-Grounded-SAM and SD-SAM 3. SD-Grounded-SAM further applies geometric and depth-aware box refinement before mask generation, while SD-SAM 3 relies on SAM 3's internal setup. On TOW-300, a dataset of 340 communication-tower UAV images, our strategy improves both ba

arXiv:2608.25319v1 Announce Type: cross Abstract: UAV displacement attacks have traditionally relied on spoofing sensors that directly report position or translational motion, such as GNSS and optical flow. In this work, we introduce SonicNudge, a new attack primitive that instead targets the gyroscope and shows that low-level inertial errors can be transformed into controlled displacement of hovering or slow-moving UAVs. The attack exploits estimator--controller coupling: a small gyroscope perturbation by ultrasonic resonance can persist as an attitude-estimation bias, and the flight controller can convert this biased estimate into a shifted hover point. This behavior is especially relevant to UAV tasks that require hovering, station-keeping, slow approach, or precise final alignment, such as perimeter denial, inspection, docking, landing alignment, and close-proximity operation, where meter-scale position errors can be operationally meaningful. We analyze this attack primitive in a P

arXiv:2608.24905v1 Announce Type: cross Abstract: Service robots may encounter ambiguous user requests that require context-aware inference. Users may also have unique preferences with certain tasks when requesting robotic assistance. We introduce PARAssist (Personalized and Adaptive Robotic Assistance), a unique architecture for disambiguating requests in a personalized manner for service robots. PARAssist utilizes vision-language models to determine the physical and cognitive demands of a user's tasks, and passively learns user preferences for assistance by contrasting the demands of tasks the user performs independently with those they request from the robot. When an ambiguous request is received, task candidates are generated from the history of the user's actions, activities, locations, conversations, and requests, as well as the current user and environment state. Task candidates are then evaluated against the learned user preference model to suggest suitable assistance options.

arXiv:2608.26103v1 Announce Type: new Abstract: Zero-shot cross-task generalization, where a policy must execute manipulation tasks never seen during training, remains a central challenge in robot learning. In large language models, a novel task can be performed simply by specifying it in the context, without any parameter update. This form of in-context learning (ICL) turns generalization into a problem of task specification. To achieve cross-task generalization, we bring this paradigm to robotic manipulation, and argue that the natural task specification for manipulation is a human video: unlike language, it provides rich visual cues about the intended task evolution. We present Zero-WAM, a causal video-action model that executes unseen tasks by following in-context human video guidance. To address the scarcity of task-rich paired human-robot data, we propose an automatic pipeline that converts task-sampled robot trajectories into semantically matched human videos, yielding HumanGen,

arXiv:2608.26076v1 Announce Type: new Abstract: Grasp synthesis is a core task in robotic manipulation, for which the solution typically forms a multimodal distribution rather than a point estimate. Generative robotic grasping aims to learn this distribution with deep generative models such as diffusion and flow-based approaches. The iterative nature of such generative models makes them flexible and generalizable; however, multi-step sampling impedes the time-critical operation required in robotics. We devise an approach to fast generative grasping based on MeanFlow on the product Lie group $\mathcal{G} = \mathrm{SO}(3) \times \mathbb{R}^3$. The training objective couples a purely algebraic semigroup consistency condition with Riemannian Conditional Flow Matching on $\mathcal{G}$ that anchors the average velocity to the data distribution. The resulting Lie Group-constrained MeanFlow formulation samples reliable grasps in $\leq 5$ network evaluations, matching the grasp generation perfo

arXiv:2608.26066v1 Announce Type: new Abstract: In this paper, we present VirTooS, a Python/C# toolkit designed to implement fleet-management tasks on teams of Autonomous Mobile Robots (AMRs). VirTooS leverages the Robot Operating System (ROS) 2 and Unity game engine to provide realistic, scalable virtual experiments in a mixed-reality environment. The toolbox allows users to easily generate and customize virtual scenarios for realistic simulations. Virtual and real sensors as, e.g., LiDARs, can be exploited to map and safely navigate in the mixed-reality environment. To enable distributed robotics experiments, we propose a set of tailored routines leveraging the ChoiRbot framework. As a motivating example, we show a set of experiments for task assignment problems in a virtual environment, allowing seamless interaction among real and virtual robots. Moreover, the package comes with a containerized suite to easily deploy it on different machines. The source code will be made publicly av

arXiv:2608.26058v1 Announce Type: new Abstract: Scaling generalist vision-language-action (VLA) policies is severely bottlenecked by the inherent heterogeneity of embodied data, which spans diverse robot morphologies, camera configurations, and low-level action spaces. Existing paradigms typically address this mismatch through explicit action retargeting, human-to-robot video synthesis, or dataset-specific adaptation branches, fundamentally hindering the joint learning of a unified policy. We introduce UCAG-P, a camera-centric unified action formulation that structurally aligns heterogeneous embodied datasets into a shared geometric action space. Rather than treating robot-specific commands as the shared policy target, UCAG-P represents manipulation through camera-observable anchor motion in image and camera-frame coordinates, treating robot arms, humanoids, and human hands as different embodiments of a common action schema. A geometry-conditioned action translator combines predicted m

arXiv:2608.26053v1 Announce Type: new Abstract: Reasoning in language allows foundation models to spend more test-time compute on hard problems, such as those requiring decomposition, constraint tracking, and prediction of future consequences. Whether this mechanism can improve robotic manipulation remains unclear, where long-horizon tasks require tracking partial progress, reasoning about object relations, recovering from mistakes, and steering noisy low-level policies. In this paper, we study whether VLMs can be trained to reason directly in natural language to guide low-level manipulation policies. We introduce $R^3$, a simple post-training recipe that turns off-the-shelf VLMs into robotic reasoners: it first mid-trains a VLM on expert-generated reasoning traces to initialize the desired reasoning style, then improves the reasoner with single-step rubric-based RL from offline action data. Unlike prior robotic reasoning methods that mostly use structured traces as auxiliary supervisi

arXiv:2608.26050v1 Announce Type: new Abstract: Autonomous robots operating in natural environments must often interact with vegetation rather than simply avoid it. In this context, traversability is typically defined from the robot's perspective, by measuring how a specific platform responds when moving through the environment. While practical, this viewpoint entangles the assessment of the environment with the robot's own dynamics, making the resulting characterization difficult to transfer across different platforms. More importantly, it does not directly reflect the properties of the vegetation itself, which are the true source of interaction and potential damage in applications such as agriculture and environmental monitoring. To address this limitation, we propose to characterize vegetation through its intrinsic mechanical properties, independently of any specific robot. By combining deformation measurements with contact force data, we estimate the underlying mechanical parameter