See Something, Say Something: Context-Criticality-Aware Mobile Robot Communication for Hazard Mitigations

The proverb ``see something, say something'' captures a core responsibility of autonomous mobile robots in safety-critical situations: when they detect a hazard, they must communicate--and do so quickly. In emergency scenarios, delayed or miscalibrated responses directly increase the time to action and the risk of damage. We argue that a systematic context-sensitive assessment of the criticality level, time sensitivity, and feasibility of mitigation is necessary for AMRs to reduce time to action and respond effectively. This paper presents a framework in which VLM/LLM-based perception drives adaptive message generation, for example, a knife in a kitchen produces a calm acknowledgment; the same object in a corridor triggers an urgent coordinated alert. Validation in 60+ runs using a patrolling mobile robot not only empowers faster response, but also brings user trusts to 82\% compared to fixed-priority baselines, validating that structured criticality assessment improves both response speed and mitigation effectiveness.

Bootstrap Perception Under Hardware Depth Failure for Indoor Robot Navigation

We present a bootstrap perception system for indoor robot navigation under hardware depth failure. In our corridor data, the time-of-flight camera loses up to 78% of its depth pixels on reflective surfaces, yet a 2D LiDAR alone cannot sense obstacles above its scan plane. Our system exploits a self-referential property of this failure: the sensor's surviving valid pixels calibrate learned monocular depth to metric scale, so the system fills its own gaps without external data. The architecture forms a failure-aware sensing hierarchy, conservative when sensors work and filling in when they fail: LiDAR remains the geometric anchor, hardware depth is kept where valid, and learned depth enters only where needed. In corridor and dynamic pedestrian evaluations, selective fusion increases costmap obstacle coverage by 55-110% over LiDAR alone. A compact distilled student runs at 218\,FPS on a Jetson Orin Nano and achieves 9/10 navigation success with zero collisions in closed-loop simulation, matching the ground-truth depth baseline at a fraction of the foundation model's cost.

A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection

Semantic anomalies-context-dependent hazards that pixel-level detectors cannot reason about-pose a critical safety risk in autonomous driving. We propose a \emph{semantic observer layer}: a quantized vision-language model (VLM) running at 1--2\,Hz alongside the primary AV control loop, monitoring for semantic edge cases, and triggering fail-safe handoffs when detected. Using Nvidia Cosmos-Reason1-7B with NVFP4 quantization and FlashAttention2, we achieve ~500 ms inference a ~50x speedup over the unoptimized FP16 baseline (no quantization, standard PyTorch attention) on the same hardware--satisfying the observer timing budget. We benchmark accuracy, latency, and quantization behavior in static and video conditions, identify NF4 recall collapse (10.6%) as a hard deployment constraint, and a hazard analysis mapping performance metrics to safety goals. The results establish a pre-deployment feasibility case for the semantic observer architecture on embodied-AI AV platforms.

Motion Planning and Control of an Overactuated 4-Wheel Drive with Constrained Independent Steering

This paper addresses motion planning and con- trol of an overactuated 4-wheel drive train with independent steering (4WIS) where mechanical constraints prevent the wheels from executing full 360-degree rotations (swerve). The configuration space of such a robot is constrained and contains discontinuities that affect the smoothness of the robot motion. We introduce a mathematical formulation of the steering constraints and derive discontinuity planes that partition the velocity space into regions of smooth and efficient motion. We further design the motion planner for path tracking and ob- stacle avoidance that explicitly accounts for swerve constraints and the velocity transition smoothness. The motion controller uses local feedback to generate actuation from the desired velocity, while properly handling the discontinuity crossing by temporarily stopping the motion and repositioning the wheels. We implement the proposed motion planner as an extension to ROS Navigation package and evaluate the system in simulation and on a physical robot.

Trust Through Transparency: Explainable Social Navigation for Autonomous Mobile Robots via Vision-Language Models

Service and assistive robots are increasingly being deployed in dynamic social environments; however, ensuring transparent and explainable interactions remains a significant challenge. This paper presents a multimodal explainability module that integrates vision language models and heat maps to improve transparency during navigation. The proposed system enables robots to perceive, analyze, and articulate their observations through natural language summaries. User studies (n=30) showed a preference of majority for real-time explanations, indicating improved trust and understanding. Our experiments were validated through confusion matrix analysis to assess the level of agreement with human expectations. Our experimental and simulation results emphasize the effectiveness of explainability in autonomous navigation, enhancing trust and interpretability.

A Virtual Fencing Framework for Safe and Efficient Collaborative Robotics

Collaborative robots (cobots) increasingly operate alongside humans, demanding robust real-time safeguarding. Current safety standards (e.g., ISO 10218, ANSI/RIA 15.06, ISO/TS 15066) require risk assessments but offer limited guidance for real-time responses. We propose a virtual fencing approach that detects and predicts human motion, ensuring safe cobot operation. Safety and performance tradeoffs are modeled as an optimization problem and solved via sequential quadratic programming. Experimental validation shows that our method minimizes operational pauses while maintaining safety, providing a modular solution for human-robot collaboration.

LLM-Enhanced Path Planning: Safe and Efficient Autonomous Navigation with Instructional Inputs

Autonomous navigation guided by natural language instructions is essential for improving human-robot interaction and enabling complex operations in dynamic environments. While large language models (LLMs) are not inherently designed for planning, they can significantly enhance planning efficiency by providing guidance and informing constraints to ensure safety. This paper introduces a planning framework that integrates LLMs with 2D occupancy grid maps and natural language commands to improve spatial reasoning and task execution in resource-limited settings. By decomposing high-level commands and real-time environmental data, the system generates structured navigation plans for pick-and-place tasks, including obstacle avoidance, goal prioritization, and adaptive behaviors. The framework dynamically recalculates paths to address environmental changes and aligns with implicit social norms for seamless human-robot interaction. Our results demonstrates the potential of LLMs to design context-aware system to enhance navigation efficiency and safety in industrial and dynamic environments.

Safety Verification for Evasive Collision Avoidance in Autonomous Vehicles with Enhanced Resolutions

This paper presents a comprehensive hazard analysis, risk assessment, and loss evaluation for an Evasive Minimum Risk Maneuvering (EMRM) system designed for autonomous vehicles. The EMRM system is engineered to enhance collision avoidance and mitigate loss severity by drawing inspiration from professional drivers who perform aggressive maneuvers while maintaining stability for effective risk mitigation. Recent advancements in autonomous vehicle technology demonstrate a growing capability for high-performance maneuvers. This paper discusses a comprehensive safety verification process and establishes a clear safety goal to enhance testing validation. The study systematically identifies potential hazards and assesses their risks to overall safety and the protection of vulnerable road users. A novel loss evaluation approach is introduced, focusing on the impact of mitigation maneuvers on loss severity. Additionally, the proposed mitigation integrity level can be used to verify the minimum-risk maneuver feature. This paper applies a verification method to evasive maneuvering, contributing to the development of more reliable active safety features in autonomous driving systems.