arXiv:2608. 00406v1 Announce Type: cross Abstract: Accurate indoor localization is essential for emerging applications in robotic navigation and search and rescue.
The paper introduces Physics‑Guided Visual Prompting (PG‑VP), a plug‑and‑play module that overlays a virtual obstacle onto the input of a frozen Vision‑Language‑Action model to guide navigation around invisible hazards such as radiation or temperature spikes. PG‑VP performs a physics‑based risk assessment to determine the avoidance direction and dynamically renders the same virtual obstacle across frames, allowing the existing navigation policy to detour without retraining. Experiments on OmniNav with R2R‑CE and RxR‑CE datasets show that PG‑VP steers the policy toward low‑risk actions in 84.9% and 83.2% of cases, while real‑world tests on a robot demonstrate significant safety improvements against thermal and radiation sources.
Robots deployed in delivery, campus, and emergency-response settings often need to navigate from buildings to streets within a single continuous episode. Existing benchmarks usually evaluate indoor and outdoor navigation separately, and many abstract away robot execution, leaving exit finding, boundary traversal, adaptation, and kinodynamic failures underexplored.
arXiv:2603. 25937v2 Announce Type: replace-cross Abstract: Visual Navigation Models (VNMs) promise generalizable, robot navigation by learning from large-scale visual demonstrations.
Hydra introduces a marker‑free RGB‑D hand‑eye calibration method that leverages a novel ICP algorithm with a robust point‑to‑plane objective on a Lie algebra. Experiments on three serial manipulators and two RGB‑D cameras show that with only three random robot configurations the method achieves about 90% successful calibrations, 2–3× faster convergence to the global optimum, and 2 orders of magnitude faster convergence time (0.8 ± 0.4 s) compared to other marker‑free baselines. The approach delivers improved accuracy (5 mm in task space versus 7 mm for classical methods) while remaining marker‑free, and the authors provide an open‑source dataset, code, and ROS 2 integration.
arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. Th...
Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through visual perception. A representative example is Human...
arXiv:2609.23974v1 Announce Type: new Abstract: Foundation models are endowing autonomous systems with greater intelligence, enabling a more comprehensive understanding of the environment through vis...
The paper presents a vision‑language navigation system that transfers from simulation to a real Ackermann‑steered mobile robot without relying on navigation graphs or panoramic views. It uses a Cross‑Modal Attention architecture trained on simulated data and fine‑tuned with limited real‑world episodes, leveraging linear photometric adjustments and a camera‑LiDAR sensor suite. Evaluation with SPL and nDTW metrics shows robust, adaptable navigation in continuous environments.
arXiv:2609.32923v2 Announce Type: replace Abstract: Wireless digital twins (DTs) rely on 3D environment models to predict radio propagation and support wireless-network decisions, yet these models ar...
The paper introduces a minimalist visual-inertial odometry system that uses only four downward-facing photodiodes with optical Gabor masks and an IMU to estimate motion for differential-drive robots. By jointly optimizing mask parameters and a Temporal Convolutional Network in a physically-grounded simulator, the model decodes speed from the photodiode signals and combines it with IMU angular speed to produce a continuous planar trajectory. Experiments on a prototype robot across indoor and outdoor terrains show that the system closely follows reference trajectories without real-world fine-tuning.
UniTrackPLA introduces a unified panorama-language-action model that simultaneously handles instruction‑guided navigation and dynamic person tracking for embodied robots. Its Panoramic‑Aware Encoding preserves azimuthal and temporal structure, allowing a shared vision‑language backbone to generate continuous waypoint chunks for both tasks. The model also employs World‑Action Consistency to predict future visual states and verify waypoint prefixes, enabling reliable action reuse and replanning when inconsistencies arise. A new OmniTrackNav‑Bench dataset and extensive real‑world experiments demonstrate significant performance gains over prior methods.