New robotics and physical-AI papers, with what each one means for the industry.
3D semantic occupancy prediction is crucial for autonomous driving, but camera-based methods degrade in low-light conditions and LiDAR-based methods are sensitive to adverse weather. 4D radar is more robust, yet its sparsity and the high cost of radar tensor processing limit its use.
Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located under the array, including connectors, cabling, fasteners and structural elements. These assets are difficult to inspect consistently at scale, and their defects are often spatially small, partially occluded and difficult to localise from isolated images.
Sonar was one of the first sensors with which mobile robots built maps, and in-air sonar keeps working where cameras and lidar fail, in darkness, smoke, dust and next to glass. Classical sonar SLAM, however, relied on geometric features or occupancy grids built from sparse range readings, and both struggle with the specular reflections that dominate indoor sonar.
Generalist visual-navigation policies tell a robot which direction to go but not how far. A separate lifting step is needed at deployment to convert their unitless trajectory into a metric path that a controller can track; existing scalar lifters apply uniform scaling that misses per-waypoint corrections, while in-policy approaches that inject monocular depth into the policy inherit the prediction errors of the depth model near obstacles and at close range.
Launching a micro unmanned aerial vehicle (UAV) from a hovering carrier and recovering it in flight requires reliable confirmation that the child is securely seated before the carrier is allowed to descend. This is challenging because vision provides relative guidance but does not directly indicate load transfer, and a downward-facing camera can lose its fiducial near touchdown.
Quadruped robots deployed for inspection and field missions must cross heterogeneous terrain, from stairs, slopes, and gaps to high platforms, stepping stones, and balance beams, where safe support is sparse or see-through. Existing methods interpose a terrain map or a depth image between sensing and control.
Light Detection and Ranging (LiDAR) is critical for autonomous perception, yet its performance is known to degrade in adverse weather. While existing literature models snow as atmospheric noise that degrades performance, our granular evaluation on the WADS-3D and CADC datasets reveals a counter-intuitive phenomenon: detection accuracy consistently improves as snow intensity increases.
LiDAR-inertial odometry (LIO) is widely used for real-time localization and mapping, but its reliability can degrade substantially in subterranean environments affected by smoke, dust, and other airborne obscurants, whereas radar remains comparatively robust. This article presents a one-class temporal learning framework for LiDAR degradation detection and reliability-aware localization under such conditions.
Nearly every autonomous or semi-autonomous flipper controller assumes access to an elevation map of the terrain under and in front of the robot. The terrain beneath the chassis is occluded in the sensors, so the map is remembered through odometry integration, whose drift corrupts it.
This preprint presents DR-IPC, a LiDAR-based quadrotor navigation stack that feeds online disturbance estimates into a nonlinear model-predictive controller for joint obstacle-aware motion and control. In Gazebo multi-goal trials with wind and a suspended payload, DR-IPC completes 9 of 10 missions versus 1 of 10 for the prior IPC baseline, and in an indoor archway experiment it reduces altitude RMSE from 0.34 m to 0.01 m. The system runs onboard at 100 Hz, making disturbance-resilient cluttered flight more practical.
GlassGuard is a preprint system that reconstructs planar architectural glass for LiDAR navigation by combining a pruned SAM3 vision model, LiDAR-derived structural pillars, depth-free orientation checks, and persistent map management. On nine real-world scenes and 2.1 km of traversal, its panoramic version covers 85.2% of glass with 15.3 false voxels per frame, while the pinhole version covers 82.1% versus at most 61.0% for baselines and carries 5–17× fewer false voxels per frame. It matters because it blocks collision paths through glass without contaminating traversable space, a dual requirement for safe robots in glass-walled buildings.
This preprint describes a lightweight onboard guidance system for micro aerial vehicles in unknown environments, combining a guiding vector field for nominal motion with panel-method potential-flow obstacle avoidance. In indoor flight tests at 0.5 m/s, a quadrotor using only a Livox MID-360 LiDAR and Raspberry Pi 5 successfully completed waypoint-navigation and direction-following tasks while avoiding all obstacles. The result matters because it shows map-free sense-and-avoid can run in real time on constrained onboard hardware, reducing operator workload for cluttered or emergency missions.
Preprint. The authors built ALFRED, an open-source mobile manipulator made from commercial parts, and ran it for a year of monthly forest surveys (528 traversals) with no missed collection. Model-based comparisons across three builds show usable arm workspace rising from 34.0% to 66.1% of reachable poses and only the final build keeps the frame-mounted LiDAR clear forward and backward. This matters because it provides a reproducible, requirement-driven platform and release package for repeated close-range plant monitoring.
This preprint introduces SonarVoxNet, a 3D sonar detector that predicts full 9-degree-of-freedom oriented bounding boxes for divers, plus Diver3D, a public 37,010-frame dataset with full 3D orientation labels. With augmentation and causal refinement, SonarVoxNet reaches 0.774 [email protected] and 0.719 SDS on the held-out test set, far above LiDAR-derived baselines that top out at 0.374 [email protected]. The work shows that pitch and roll of a freely swimming diver can be recovered from sparse acoustic returns, which matters for AUV diver-robot interaction.
This preprint presents TACTIC, a scene-aware framework that uses a multimodal large language model and roadside perception to choose and tune physical LiDAR attacks in dynamic traffic. In 280 randomized CARLA trials, the full system caused collisions in 100% of evaluation cases, versus 35% for a fixed rule and 60% for random attack selection. The result shows attack success depends on scene context, which matters for evaluating LiDAR safety failures in autonomous driving.
World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constraint on these predictions. We present PhysWAM, a unified world-action model for autonomous driving that co-denoises multiview video, metric depth, and ego motion within a single flow-matching transformer.
Papers come from arXiv robotics feeds. Where our summary is not written yet, you see the opening lines of the abstract.