Safe Robot Navigation

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Overview

Safe robot navigation studies how robots can reach goals without collisions while accounting for limited sensing, uncertain motion, changing obstacles, and hazards such as people or difficult terrain. Common approaches include control barrier functions and reachability analysis for enforcing safety constraints, model predictive control and graph search for planning, and learned visual or language-guided policies for interpreting unfamiliar scenes. Research increasingly combines these methods, using semantic risk maps, human-motion prediction, uncertainty calibration, or runtime safety filters to make decisions in dynamic and partially observed settings. A central challenge is balancing formal guarantees and reliable evaluation with the adaptability and efficiency of learned methods, which can become overly cautious or fail when predictions, timing, or real-world conditions differ from assumptions.

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141 papers23 papers in the last four weeks, up 35% on the four weeks before. 0.1% of all new papers.

Mar 6, 2026

Control Barrier Corridors: From Safety Functions to Safe Sets

Safe autonomy is a critical requirement and a key enabler for robots to operate safely in unstructured complex environments. Control barrier functions and safe motion corridors are two widely used but technically distinct safety methods, functional and geometric, respectively, for safe motion planning and control. Control barrier functions are applied to the safety filtering of control inputs to limit the decay rate of system safety, whereas safe motion corridors are geometrically constructed to define a local safe zone around the system state for use in motion optimization and reference-governor design. This paper introduces a new notion of control barrier corridors, which unifies these two approaches by converting control barrier functions into local safe goal regions for reference goal selection in feedback control systems. We show, with examples on fully actuated systems, kinematic unicycles, and linear output regulation systems, that individual state safety can be extended locally over control barrier corridors for convex barrier functions, provided the control convergence rate matches the barrier decay rate, highlighting a trade-off between safety and reactiveness. Such safe control barrier corridors enable safely reachable persistent goal selection over continuously changing barrier corridors during system motion, which we demonstrate for verifiably safe and persistent path following in autonomous exploration of unknown environments.

Ömür Arslan, Nikolay Atanasov

Department of Mechanical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands. · Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Control Barrier FunctionsSafe Motion PlanningSafe Robot Navigation
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Mar 6, 2026

Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds

Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-of-distribution crowd sizes due to density-sensitive observation normalization and social-cost scaling, while analytical solvers often remain safe but freeze in tight interactions. We propose a reinforcement learning approach for dense, variable-density navigation that attains zero-shot density generalization using a density-invariant observation encoding with density-randomized training and physics-informed proxemic reward shaping with density-adaptive scaling. The encoding represents the distance-sorted KK nearest pedestrians plus bounded crowd summaries, keeping input statistics stable as crowd size grows. Trained with N ⁣∈ ⁣[11,16]N\!\in\![11,16] pedestrians in a 3m×3m3\mathrm{m}\times3\mathrm{m} arena and evaluated up to N ⁣= ⁣21N\!=\!21 pedestrians (1.3×1.3\times denser), our policy reaches the goal in >99%>99\% of episodes and achieves 86%86\% collision-free success in random crowds, with markedly less freezing than analytical methods and a > ⁣60>\!60-point collision-free margin over learning-based benchmark methods. Codes are available at https://github.com/jznmsl/PSS-Social.

Jiefu Zhang, Yang Xu, Vaneet Aggarwal

Purdue University, USA

Safe Robot NavigationRobot NavigationRobot Collision AvoidanceRobot Policy LearningRobot Policy Generalization
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Mar 5, 2026

Safe-SAGE: Social-Semantic Adaptive Guidance for Safe Engagement through Laplace-Modulated Poisson Safety Functions

Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around obstacles regardless of contextual significance. This limitation leads to the uniform treatment of all obstacles, despite their differing semantic meanings. We present Safe-SAGE (Social-Semantic Adaptive Guidance for Safe Engagement), a unified framework that bridges the gap between high-level semantic understanding and low-level safety-critical control through a Poisson safety function (PSF) modulated using a Laplace guidance field. Our approach perceives the environment by fusing multi-sensor point clouds with vision-based instance segmentation and persistent object tracking to maintain up-to-date semantics beyond the camera's field of view. A multi-layer safety filter is then used to modulate system inputs to achieve safe navigation using this semantic understanding of the environment. This safety filter consists of both a model predictive control layer and a control barrier function layer. Both layers utilize the PSF and flux modulation of the guidance field to introduce varying levels of conservatism and multi-agent passing norms for different obstacles in the environment. Our framework enables legged robots to safely navigate semantically rich, dynamic environments with context-dependent safety margins.

Lizhi Yang, Ryan M. Bena, Meg Wilkinson +4

Caltech MCE · Tufts ME

Robot NavigationControl Barrier FunctionsSafe Robot NavigationSocial Robot NavigationRobot Safety
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Mar 5, 2026

U-OBCA: Uncertainty-Aware Optimization-Based Collision Avoidance via Wasserstein Distributionally Robust Chance Constraints

Uncertainties arising from localization error, trajectory prediction errors of the moving obstacles and environmental disturbances pose significant challenges to robot's safe navigation. Existing uncertainty-aware planners often approximate polygon-shaped robots and obstacles using simple geometric primitives such as circles or ellipses. Though computationally convenient, these approximations substantially shrink the feasible space, leading to overly conservative trajectories and even planning failure in narrow environments. In addition, many such methods rely on specific assumptions about noise distributions, which may not hold in practice and thus limit their performance guarantees. To address these limitations, we extend the Optimization-Based Collision Avoidance (OBCA) framework to an uncertainty-aware formulation, termed U-OBCA. The proposed method explicitly accounts for the collision risk between polygon-shaped robots and obstacles by formulating OBCA-based chance constraints, and hence avoiding geometric simplifications and reducing unnecessary conservatism. These probabilistic constraints are further tightened into deterministic nonlinear constraints under mild distributional assumptions, which can be solved efficiently by standard numerical optimization solvers. The proposed approach is validated through theoretical analysis, numerical simulations and real-world experiments. The results demonstrate that U-OBCA significantly mitigates the conservatism in trajectory planning and achieves higher navigation efficiency compared to existing baseline methods, particularly in narrow and cluttered environments.

Zehao Wang, Yuxuan Tang, Han Zhang +2

School of Automation and Intelligent Sensing, Shanghai Jiao Tong University and Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai 200240, China. · Key Laboratory of System Control and Information Processing, Ministry of Education of China, and Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, China.

Chance-Constrained Motion PlanningCollision AvoidanceRobot Collision AvoidanceSafe Robot NavigationMotion Planning
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Mar 4, 2026

Gaussian Mixture-Based Inverse Perception Contract for Uncertainty-Aware Robot Navigation

Reliable navigation in cluttered environments requires perception outputs that are not only accurate but also equipped with uncertainty sets suitable for safe control. An inverse perception contract (IPC) provides such a connection by mapping perceptual estimates to sets that contain the ground truth with high confidence. Existing IPC formulations, however, instantiate uncertainty as a single ellipsoidal set and rely on deterministic trust scores to guide robot motion. Such a representation cannot capture the multi-modal and irregular structure of fine-grained perception errors, often resulting in over-conservative sets and degraded navigation performance. In this work, we introduce Gaussian Mixture-based Inverse Perception Contract (GM-IPC), which extends IPC to represent uncertainty with unions of ellipsoidal confidence sets derived from Gaussian mixture models. This design moves beyond deterministic single-set abstractions, enabling fine-grained, multi-modal, and non-convex error structures to be captured with formal guarantees. A learning framework is presented that trains GM-IPC to account for probabilistic inclusion, distribution matching, and empty-space penalties, ensuring both validity and compactness of the predicted sets. We further show that the resulting uncertainty characterizations can be leveraged in downstream planning frameworks for real-time safe navigation, enabling less conservative and more adaptive robot motion while preserving safety in a probabilistic manner.

Bingyao Du, Joonkyung Kim, Yiwei Lyu

The author is with the Department of Computer Science, Columbia University, New York, NY 10027, USA. · The authors are with the Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77843, USA.

Uncertainty QuantificationRobotic PerceptionSafe Robot Navigation
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Mar 4, 2026

RVN-Bench: A Benchmark for Reactive Visual Navigation

Safe visual navigation is critical for indoor mobile robots operating in cluttered environments. Existing benchmarks, however, often neglect collisions or are designed for outdoor scenarios, making them unsuitable for indoor visual navigation. To address this limitation, we introduce the reactive visual navigation benchmark (RVN-Bench), a collision-aware benchmark for indoor mobile robots. In RVN-Bench, an agent must reach sequential goal positions in previously unseen environments using only visual observations and no prior map, while avoiding collisions. Built on the Habitat 2.0 simulator and leveraging high-fidelity HM3D scenes, RVN-Bench provides large-scale, diverse indoor environments, defines a collision-aware navigation task and evaluation metrics, and offers tools for standardized training and benchmarking. RVN-Bench supports both online and offline learning by offering an environment for online reinforcement learning, a trajectory image dataset generator, and tools for producing negative trajectory image datasets that capture collision events. Evaluations demonstrate that policies trained on RVN-Bench generalize effectively across unseen simulated environments. Furthermore, initial physical experiments using a Jackal UGV indicate promising sim-to-real transfer. Code and additional materials are available at: https://sequor-robotics-research.github.io/projects/RVN-Bench/.

Jaewon Lee, Jaeseok Heo, Gunmin Lee +3

Department of Electrical and Computer Engineering, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc., Seoul, Korea (Republic of). · Department of Electrical and Computer Engineering, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI), Seoul, Korea (Republic of). · Interdisciplinary Program in Artificial Intelligence, Seoul National University (SNU) and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc., Seoul, Korea (Republic of). +1

Robot Navigation BenchmarksRobot NavigationVisual NavigationIndoor Robot NavigationSafe Robot NavigationRobot Collision Avoidance
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Mar 2, 2026

CHOP: Counterfactual Human Preference Labels Improve Obstacle Avoidance in Visuomotor Navigation Policies

Visuomotor navigation policies have shown strong perception-action coupling for embodied agents, yet they often struggle with safe navigation and dynamic obstacle avoidance in complex real-world environments. We introduce CHOP, a novel approach that leverages Counterfactual Human Preference Labels to align visuomotor navigation policies towards human intuition of safety and obstacle avoidance in navigation. In CHOP, for each visual observation, the robot's executed trajectory is included among a set of counterfactual navigation trajectories: alternative trajectories the robot could have followed under identical conditions. Human annotators provide pairwise preference labels over these trajectories based on anticipated outcomes such as collision risk and path efficiency. These aggregated preferences are then used to fine-tune visuomotor navigation policies, aligning their behavior with human preferences in navigation. Experiments on the SCAND dataset show that visuomotor navigation policies fine-tuned with CHOP reduce near-collision events by 49.7%, decrease deviation from human-preferred trajectories by 45.0%, and increase average obstacle clearance by 19.8% on average across multiple state-of-the-art models, compared to their pretrained baselines. These improvements transfer to real-world deployments on a Ghost Robotics Vision60 quadruped, where CHOP-aligned policies improve average goal success rates by 24.4%, increase minimum obstacle clearance by 6.8%, reduce collision and intervention events by 45.7%, and improve normalized path completion by 38.6% on average across navigation scenarios, compared to their pretrained baselines. Our results highlight the value of counterfactual preference supervision in bridging the gap between large-scale visuomotor policies and human-aligned, safety-aware embodied navigation.

Gershom Seneviratne, Jianyu An, Vaibhav Shende +6

University of Maryland, College Park

Safe Robot NavigationVisuomotor Policy LearningDynamic Obstacle AvoidanceHuman Preference Modeling
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Mar 2, 2026

SaferPath: Hierarchical Visual Navigation with Learned Guidance and Safety-Constrained Control

Visual navigation is a core capability for mobile robots, yet end-to-end learning-based methods often struggle with generalization and safety in unseen, cluttered, or narrow environments. These limitations are especially pronounced in dense indoor settings, where collisions are likely and end-to-end models frequently fail. To address this, we propose SaferPath, a hierarchical visual navigation framework that leverages learned guidance from existing end-to-end models and refines it through a safety-constrained optimization-control module. SaferPath transforms visual observations into a traversable-area map and refines guidance trajectories using Model Predictive Stein Variational Evolution Strategy (MP-SVES), efficiently generating safe trajectories in only a few iterations. The refined trajectories are tracked by an MPC controller, ensuring robust navigation in complex environments. Extensive experiments in scenarios with unseen obstacles, dense unstructured spaces, and narrow corridors demonstrate that SaferPath consistently improves success rates and reduces collisions, outperforming representative baselines such as ViNT and NoMaD, and enabling safe navigation in challenging real-world settings.

Lingjie Zhang, Zeyu Jiang, Changhao Chen

PEAK-Lab, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511453, China

Visual Robot NavigationRobot NavigationSafe Robot NavigationConstrained Motion Planning
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Mar 1, 2026

Fast Confidence-Aware Human Prediction via Hardware-accelerated Bayesian Inference for Safe Robot Navigation

As robots increasingly integrate into everyday environments, ensuring their safe navigation around humans becomes imperative. Efficient and safe motion planning requires robots to account for human behavior, particularly in constrained spaces such as grocery stores or care homes, where interactions with multiple individuals are common. Prior research has employed Bayesian frameworks to model human rationality based on navigational intent, enabling the prediction of probabilistic trajectories for planning purposes. In this work, we present a simple yet novel approach for confidence-aware prediction that treats future predictions as particles. This framework is highly parallelized and accelerated on an graphics processing unit (GPU). As a result, this enables longer-term predictions at a frequency of 125 Hz and can be easily extended for multi-human predictions. Compared to existing methods, our implementation supports finer prediction time steps, yielding more granular trajectory forecasts. This enhanced resolution allows motion planners to respond effectively to subtle changes in human behavior. We validate our approach through real-world experiments, demonstrating a robot safely navigating among multiple humans with diverse navigational goals. Our results highlight the methods potential for robust and efficient human-robot coexistence in dynamic environments.

Michael Lu, Minh Bui, Xubo Lyu +1

School of Computing Science, Simon Fraser University, Canada

Probabilistic Trajectory PredictionHuman Motion PredictionTrajectory PredictionSafe Robot NavigationMulti-Agent Trajectory Prediction
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Feb 28, 2026

Optimal-Horizon Social Robot Navigation in Heterogeneous Crowds

Navigating social robots in dense, dynamic crowds is challenging due to environmental uncertainty and complex human-robot interactions. While Model Predictive Control (MPC) offers strong real-time performance, its reliance on a fixed prediction horizon limits adaptability to changing environments and social dynamics. Furthermore, most MPC approaches treat pedestrians as homogeneous obstacles, ignoring social heterogeneity and cooperative or adversarial interactions, which often causes the Frozen Robot Problem in partially observable real-world environments. In this paper, we identify the planning horizon as a socially conditioned decision variable rather than a fixed design choice. Building on this insight, we propose an optimal-horizon social navigation framework that optimizes MPC foresight online according to inferred social context. A spatio-temporal Transformer infers pedestrian cooperation attributes from local trajectory observations, which serve as social priors for a reinforcement learning policy that optimally selects the prediction horizon under a task-driven objective. The resulting horizon-aware MPC incorporates socially conditioned safety constraints to balance navigation efficiency and interaction safety. Extensive simulations and real-world robot experiments demonstrate that optimal foresight selection is critical for robust social navigation in partially observable crowds. Compared to state-of-the-art baselines, the proposed approach achieves a 6.8% improvement in success rate, reduces collisions by 50%, and shortens navigation time by 19%, with a low timeout rate of 0.8%, validating the necessity of socially optimal planning horizons for efficient and safe robot navigation in crowded environments. Code and videos are available at Under Review.

Jiamin Shi, Haolin Zhang, Yuchen Yan +3

National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Nation Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China

Social Robot NavigationSafe Robot NavigationModel Predictive ControlRobot Policy Learning
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Feb 22, 2026

WildOS: Open-Vocabulary Object Search in the Wild

Autonomous navigation in complex, unstructured outdoor environments requires robots to operate over long ranges without prior maps and limited depth sensing. In such settings, relying solely on geometric frontiers for exploration is often insufficient. In such settings, the ability to reason semantically about where to go and what is safe to traverse is crucial for robust, efficient exploration. This work presents WildOS, a unified system for long-range, open-vocabulary object search that combines safe geometric exploration with semantic visual reasoning. WildOS builds a sparse navigation graph to maintain spatial memory, while utilizing a foundation-model-based vision module, ExploRFM, to score frontier nodes of the graph. ExploRFM simultaneously predicts traversability, visual frontiers, and object similarity in image space, enabling real-time, onboard semantic navigation tasks. The resulting vision-scored graph enables the robot to explore semantically meaningful directions while ensuring geometric safety. Furthermore, we introduce a particle-filter-based method for coarse localization of the open-vocabulary target query, that estimates candidate goal positions beyond the robot's immediate depth horizon, enabling effective planning toward distant goals. Extensive closed-loop field experiments across diverse off-road and urban terrains demonstrate that WildOS enables robust navigation, significantly outperforming purely geometric and purely vision-based baselines in both efficiency and autonomy. Our results highlight the potential of vision foundation models to drive open-world robotic behaviors that are both semantically informed and geometrically grounded. Project Page: https://leggedrobotics.github.io/wildos/

Hardik Shah, Erica Tevere, Deegan Atha +6

Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech), Pasadena, CA 91011, USA · Swiss Federal Institute of Technology (ETH Zürich), Robotic Systems Lab, Zürich 8092, Switzerland · FieldAI Inc. 3 Morgan, Irvine, CA, USA. Work Performed at NASA JPL +2

Visual Robot NavigationRobotic Object SearchOpen-Vocabulary Object NavigationAutonomous Robot ExplorationOff-Road Robot NavigationSafe Robot Navigation+1
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Feb 22, 2026

Understanding Fire Through Thermal Radiation Fields for Mobile Robots

Safely moving through environments affected by fire is a critical capability for autonomous mobile robots deployed in disaster response. In this work, we present a novel approach for mobile robots to understand fire through building real-time thermal radiation fields. We register depth and thermal images to obtain a 3D point cloud annotated with temperature values. From these data, we identify fires and use the Stefan-Boltzmann law to approximate the thermal radiation in empty spaces. This enables the construction of a continuous thermal radiation field over the environment. We show that this representation can be used for robot navigation, where we embed thermal constraints into the cost map to compute collision-free and thermally safe paths. We validate our approach on a Boston Dynamics Spot robot in controlled experimental settings. Our experiments demonstrate the robot's ability to avoid hazardous regions while still reaching navigation goals. Our approach paves the way toward mobile robots that can be autonomously deployed in fire-affected environments, with potential applications in search-and-rescue, firefighting, and hazardous material response.

Anton R. Wagner, Madhan Balaji Rao, Xuesu Xiao +1

Department of Computer Science, Kiel University, Germany · Department of Computer Science, George Mason University, USA

Mobile RoboticsRobot NavigationSafe Robot Navigation
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Feb 19, 2026

RA-Nav: A Risk-Aware Navigation System Based on Semantic Segmentation for Aerial Robots in Unpredictable Environments

Existing aerial robot navigation systems typically plan paths around static and dynamic obstacles, but fail to adapt when a static obstacle suddenly moves. Integrating environmental semantic awareness enables estimation of potential risks posed by suddenly moving obstacles. In this paper, we propose RA- Nav, a risk-aware navigation framework based on semantic segmentation. A lightweight multi-scale semantic segmentation network identifies obstacle categories in real time. These obstacles are further classified into three types: stationary, temporarily static, and dynamic. For each type, corresponding risk estimation functions are designed to enable real-time risk prediction, based on which a complete local risk map is constructed. Based on this map, the risk-informed path search algorithm is designed to guarantee planning that balances path efficiency and safety. Trajectory optimization is then applied to generate trajectories that are safe, smooth, and dynamically feasible. Comparative simulations demonstrate that RA-Nav achieves higher success rates than baselines in sudden obstacle state transition scenarios. Its effectiveness is further validated in simulations using real- world data.

Ziyi Zong, Xin Dong, Jinwu Xiang +2

Safe Robot NavigationSafe Motion PlanningDynamic Obstacle Avoidance
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Dec 24, 2025

Schrödinger's Navigator: Imagining an Ensemble of Futures for Zero-Shot Object Navigation

Zero-shot object navigation (ZSON) requires robots to find target objects in unseen environments without task-specific fine-tuning or pre-built maps, a key capability for general-purpose service robots. Yet methods that perform well in simulation often degrade in cluttered real-world scenes with severe occlusion and latent hazards, where large unseen regions make single-scene inference brittle and unsafe. We propose Schrödinger's Navigator, a belief-aware framework that reasons at inference time over multiple trajectory-conditioned imagined 3D futures. Given candidate paths, a trajectory-conditioned 3D world model predicts hypothetical observations and maintains a superposition of plausible scene realizations rather than committing to one map. An adaptive occluder-aware sampler directs imagination to uncertainty-critical regions, while a Future-Aware Value Map (FAVM) aggregates imagined futures for robust, proactive action selection. Experiments in simulation and on a physical Go2 quadruped show that Schrödinger's Navigator outperforms strong ZSON baselines, improving hidden-target discovery and risk-aware waypoint selection in occlusion-heavy navigation scenarios. These results highlight imagined 3D futures as a scalable and generalizable strategy for zero-shot navigation in uncertain real-world environments.

Yu He, Da Huang, Zhenyang Liu +5

Fudan University · Shanghai Innovation Institute · ActiMind +3

3D Scene PredictionRobot NavigationObject-Goal NavigationSafe Robot NavigationWorld Models for Robotics
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Oct 27, 2025

UrbanVLA: A Vision-Language-Action Model for Urban Micromobility

Urban micromobility applications, such as delivery robots, demand reliable navigation across large-scale urban environments while following long-horizon route instructions. This task is particularly challenging due to the dynamic and unstructured nature of real-world city areas, yet most existing navigation methods remain tailored to short-scale and controllable scenarios. Effective urban micromobility requires two complementary levels of navigation skills: low-level capabilities such as point-goal reaching and obstacle avoidance, and high-level capabilities, such as route-visual alignment. To this end, we propose UrbanVLA, a route-conditioned Vision-Language-Action (VLA) framework designed for scalable urban navigation. Our method explicitly aligns noisy route waypoints with visual observations during execution, and subsequently plans trajectories to drive the robot. To enable UrbanVLA to master both levels of navigation, we employ a two-stage training pipeline. The process begins with Supervised Fine-Tuning (SFT) using simulated environments and trajectories parsed from web videos. This is followed by Reinforcement Fine-Tuning (RFT) on a mixture of simulation and real-world data, which enhances the model's safety and adaptability in real-world settings. Experiments demonstrate that UrbanVLA surpasses strong baselines by more than 55% in the SocialNav task on MetaUrban. Furthermore, UrbanVLA achieves reliable real-world navigation, showcasing both scalability to large-scale urban environments and robustness against real-world uncertainties.

Anqi Li, Zhiyong Wang, Jiazhao Zhang +5

Peking University · Galbot · USTC +1

Robot NavigationSafe Robot NavigationSocial Robot NavigationVision-Language NavigationVLMs for Robotics
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Oct 20, 2025

HumanHalo: Safe and Efficient 3D Navigation Among Humans via Minimally Conservative MPC

Safe and efficient robotic navigation among humans is essential for integrating robots into everyday environments. Most existing approaches focus on simplified 2D crowd navigation and fail to account for the full complexity of human body dynamics beyond root motion. We present HumanHalo, an MPC framework for 3D MAV navigation among humans that combines theoretical safety guarantees with data-driven models for realistic human motion forecasting. Our approach introduces a novel reachability-based safety formulation that constrains only the initial control input for safety while modeling its effects over the entire planning horizon, enabling safe yet efficient navigation. We validate HumanHalo in both simulated experiments using real human trajectories and in the real world, demonstrating its effectiveness across tasks ranging from goal-directed navigation to visual servoing for human tracking. While we apply our method to MAV in this work, it is generic and can be adapted to other platforms. Our results show that the method preserves safety without excessive conservatism, even under imperfect human prediction and in constrained workspaces, while remaining efficient enough for real-time onboard execution.

Simon Schaefer, Helen Oleynikova, Sandra Hirche +1

Technical University of Munich · ETH Zurich

Constrained Motion PlanningUAV NavigationSafe Robot NavigationSocial Robot NavigationRobot SafetyModel Predictive Control
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Aug 26, 2025

Real-Time Model Checking for Closed-Loop Robot Reactive Planning

Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i.e., the next action based on the current state). In this paper, we use model checking to achieve reactive multi-step planning and obstacle avoidance on an autonomous robot. Our small, purpose-built model checking algorithm generates plans in situ (within the robot's code) based on ``core'' knowledge and attention as found in biological agents. This is achieved in real-time using no pre-computed data on a low-powered device. Our approach is based on chaining temporary control systems that are spawned to counteract disturbances in the local environment which disrupt an autonomous agent from its preferred action (or resting state). We mitigate state-space explosion by relying on temporary snapshots of the immediate environment, restricting the number of states. Multi-step planning using counter-examples generated by depth-first search and a negated LTL path property is applied to scenarios involving a cul-de-sac and a free-standing obstacle. Empirical results and informal proofs of two fundamental properties demonstrate the effectiveness of our approach for the creation of efficient multi-step plans for local obstacle avoidance. We significantly improve performance compared to a purely reactive agent that can only plan one step ahead. Our approach is an instructional case study for the development of safe and reliable navigation in the context of autonomous vehicles. We believe it also has general application in navigation for mission-critical mobile robots.

Christopher Chandler, Bernd Porr, Giulia Lafratta +1

Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, Scotland, UK · School of Engineering, University of Glasgow, Glasgow, Scotland, UK · School of Computing Science, University of Glasgow, Glasgow, Scotland, UK

Safe Robot NavigationFormal VerificationRobot Task PlanningRobot Collision Avoidance
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Aug 26, 2025

ZeST: A VLM-based Zero-Shot Traversability Navigation for Unknown Environments

Advances in robotics and autonomous navigation systems hinge on the ability to predict terrain traversability accurately. Traditional methods for generating datasets to train these prediction models often involve putting robots into potentially hazardous environments, posing risks to equipment and safety. To solve this problem, we present ZeST, a novel approach that treats repeated VLM outputs as stochastic measurements and fuses them into an uncertainty-aware posterior. Our approach not only performs zero-shot traversability and mitigates the risks associated with real-world data collection but also accelerates the development of advanced navigation systems, offering a cost-effective and scalable solution. To support our findings, we present navigation results, in both controlled indoor and unstructured outdoor environments. As shown in the experiments, ZeST provides safer navigation with a 90-100% success rate while no baseline exceeds 60%, and keeps complete success under query latencies up to 3.6s.

Shreya Gummadi, Mateus V. Gasparino, Gianluca Capezzuto +2

Field Robotics Engineering and Science Hub (FRESH), Illinois Autonomous Farm, University of Illinois at Urbana-Champaign (UIUC), IL. · Mobile Robotics Group, University of S˜ao Paulo, S˜ao Carlos, SP, Brazil.

Robot NavigationSafe Robot NavigationVLMs for RoboticsRobot Traversability Estimation
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Jun 8, 2025

BR-MPPI: Barrier-Rate Guided MPPI for Enforcing Multiple Inequality Constraints with Learned Signed Distance Fields

Model Predictive Path Integral (MPPI) control provides a sampling-based framework for optimal control, while Control Barrier Functions (CBFs) provide a principled means of enforcing safety constraints. We introduce BR-MPPI, which integrates CBF-like conditions into MPPI's control sampling procedure. CBFs impose inequality constraints that bound the rate of change of barrier functions using a class-K function of the barrier value. We instead impose the CBF condition as an equality constraint using a parametric linear class-K function and augment the system state with its parameter. The parameter's time derivative serves as an additional control input optimized by MPPI. We further design a cost function that promotes parameter values consistent with Nagumo's condition at the safe-set boundary, thereby encouraging safety. The resulting multiple state- and control-dependent equality constraints pose a challenge for random control sampling. We address this through state transformations and control projections inspired by manifold path planning that map sampled controls onto the constraint manifold. We also incorporate learned signed distance fields to represent robot geometry and reduce computation time. Simulations demonstrate improved sample efficiency over vanilla MPPI and higher navigation success rates across five robot models compared with safety-oriented MPPI variants. Hardware experiments on a quadrotor further demonstrate the method's ability to navigate constrained environments near safe-set boundaries.

Hardik Parwana, Taekyung Kim, Kehan Long +4

Robotics Department, University of Michigan, Ann Arbor, MI 48109, USA · Contextual Robotics Institute, University of California San Diego, La Jolla, CA 92093, USA. · Toyota Motor North America, Research & Development, Ann Arbor, MI 48105, USA.

Constrained Motion PlanningControl Barrier FunctionsSafe Robot NavigationMPPI ControlOptimal Control
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May 17, 2024

OGM-CBF: Occupancy Grid Map-based Control Barrier Function for Safe Mobile Robot Control with Memory of out of View Obstacles

Safe control in unknown environments is a key challenge in mobile robotics. Control Barrier Functions (CBFs) provide a principled framework for guaranteeing safety constraint satisfaction. State-of-the-art CBF approaches assume either known environments with predefined obstacles, or rely only on obstacles currently within the robot's Field of View (FoV). However, practical robots in a priori unknown environments can observe their surroundings only partially, and therefore can violate safety due to limited FoV, sensor range, or occlusion. This paper incorporates the memory of previously observed obstacles of arbitrary shape that have left the robot's FoV into CBF-based safe control. In particular, we couple the Signed Distance Function (SDF)-based CBF formulation to an occupancy grid map built online during the system's operation. Furthermore, the lack of steering authority induced by the SDF gradient degeneracy when facing obstacles head-on is addressed by constructing a Gaussian pyramid of the SDF, yielding a multi-level CBF. The efficacy of the proposed approach is evaluated against memory unaware baselines in the CARLA simulator. Moreover, we demonstrate the generalizability of the proposed approach in real deployments on a small warehouse robot and a large, articulated frame steering autonomous wheel loader.

Golnaz Raja, Miloš Prágr, Topi Reino Johannes Kärki +2

Faculty of Engineering and Natural Sciences, Tampere University, Kalevantie 4, 33100 Tampere, Finland

Mobile RoboticsControl Barrier FunctionsSafe Robot Navigation
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Mar 17, 2024

N-dimensional Convex Obstacle Avoidance using Hybrid Feedback Control (Extended version)

This paper addresses the autonomous robot navigation problem in a priori unknown n-dimensional environments containing disjoint convex obstacles of arbitrary shapes and sizes, with pairwise distances strictly greater than the robot's diameter. We propose a hybrid feedback control scheme that guarantees safe and global asymptotic convergence of the robot to a predefined target location. The proposed control strategy relies on a switching mechanism allowing the robot to operate either in the move-to-target mode or the obstacle-avoidance mode, based on its proximity to the obstacles and the availability of a clear straight path between the robot and the target. In the obstacle-avoidance mode, the robot is constrained to move within a two-dimensional plane that intersects the obstacle being avoided and the target, preventing it from retracing its path. The effectiveness of the proposed hybrid feedback controller is demonstrated through simulations in two-dimensional and three-dimensional environments.

Mayur Sawant, Ilia Polushin, Abdelhamid Tayebi

Department of Electrical and Computer Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada · Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada

Collision AvoidanceSafe Robot NavigationFeedback ControlRobot Collision Avoidance
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