Robotics
VLSA: Vision-Language-Action Models with Plug-and-Play Safety Constraint Layer
Songqiao Hu, Zeyi Liu, Shuang Liu et al. (+5 more)
Vision-
Language-Action (VLA) models have demonstrated remarkable capabilities in generalizing across diverse robotic manipulation tasks. However, deploying these models in unstructured environments remains challenging due to the critical need for simultaneous task compliance and safety assurance, particularly in preventing potential collisions during physical interactions. In this work, we introduce a
Vision-
Language-Safe Action (VLSA) architecture, named AEGIS, which contains a plug-and-play safety constraint (SC) layer formulated via control barrier functions. AEGIS integrates directly with existing VLA models to improve safety with theoretical guarantees, while maintaining their original instruction-following performance. To evaluate the efficacy of our architecture, we construct a comprehensive safety-critical
benchmark SafeLIBERO, spanning distinct manipulation scenarios characterized by varying degrees of spatial complexity and obstacle intervention. Extensive experiments demonstrate the superiority of our method over state-of-the-art baselines. Notably, AEGIS achieves over 50% improvement in obstacle avoidance rate while substantially increasing the task success rate by nearly 10%. All
benchmark datasets, code, and supplementary materials are publicly available at https://vlsa-aegis.github.io/.
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All authors Songqiao Hu, Zeyi Liu, Shuang Liu, Jun Cen, Zihan Meng, Shihefeng Wang, Xiang Li, Xiao He
Robotics
Enabling Autonomous Navigation in a Snake Robot through Visual-Inertial Odometry and Closed-Loop Trajectory Tracking Control
Mohammed Irfan Ali
Snake
robots offer exceptional mobility across extreme terrain inaccessible to conventional rovers, yet their highly articulated bodies present fundamental challenges for autonomous
navigation in environments lacking external tracking infrastructure. This thesis develops a complete autonomy pipeline for COBRA, an 11 degree-of-freedom modular snake
robot designed for planetary exploration. While the
robot's biologically inspired serpentine gaits achieve impressive mobility, prior work has relied entirely on open-loop teleoperation. This approach integrates onboard visual-inertial SLAM, reduced-order state estimation, and closed-loop trajectory tracking to enable autonomous waypoint
navigation. A depth camera paired with edge computing performs real-time localization during dynamic locomotion, validated against motion-capture ground truth to characterize drift behavior and failure modes unique to snake
robot platforms. A reduced-order framework estimates Center-of-Mass pose, driving a closed-loop controller that modulates CPG gait parameters through distance-dependent yaw error blending. Physical experiments validate the complete system, demonstrating accurate multi-waypoint tracking and establishing foundations for autonomous snake
robot navigation.
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Robotics
Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous
Yuji Takubo, Arpit Dwivedi, Sukeerth Ramkumar et al. (+4 more)
Reliable real-time trajectory generation is essential for future autonomous spacecraft. While recent progress in nonconvex guidance and control is paving the way for onboard autonomous trajectory optimization, these methods still rely on extensive expert input (e.g., waypoints, constraints, mission timelines, etc.), which limits operational scalability in complex missions such as rendezvous and proximity operations. This paper introduces SAGES (Semantic Autonomous Guidance Engine for Space), a trajectory-generation framework that translates natural-
language commands into spacecraft trajectories that reflect high-level intent while respecting nonconvex constraints. Experiments in two settings (fault-tolerant proximity operations with continuous-time constraint enforcement and a free-flying robotic platform) demonstrate that SAGES reliably produces trajectories aligned with human commands, achieving over 90% semantic-behavioral consistency across diverse behavior modes. Ultimately, this work marks an initial step toward
language-conditioned, constraint-aware spacecraft trajectory generation, enabling operators to interactively guide both safety and behavior through intuitive natural-
language commands with reduced expert burden. Project Website: https://semantic-guidance4space.github.io/
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All authors Yuji Takubo, Arpit Dwivedi, Sukeerth Ramkumar, Luis A. Pabon, Daniele Gammelli, Marco Pavone, Simone D'Amico
Robotics
Cognitive Trust in HRI: "Pay Attention to Me and I'll Trust You Even if You are Wrong"
Adi Manor, Dan Cohen, Ziv Keidar et al. (+2 more)
Cognitive trust and the belief that a
robot is capable of accurately performing tasks, are recognized as central factors in fostering high-quality human-
robot interactions. It is well established that performance factors such as the
robot's competence and its reliability shape cognitive trust. Recent studies suggest that affective factors, such as robotic attentiveness, also play a role in building cognitive trust. This work explores the interplay between these two factors that shape cognitive trust. Specifically, we evaluated whether different combinations of robotic competence and attentiveness introduce a compensatory mechanism, where one factor compensates for the lack of the other. In the experiment, participants performed a search task with a robotic dog in a 2x2 experimental design that included two factors: competence (high or low) and attentiveness (high or low). The results revealed that high attentiveness can compensate for low competence. Participants who collaborated with a highly attentive
robot that performed poorly reported trust levels comparable to those working with a highly competent
robot. When the
robot did not demonstrate attentiveness, low competence resulted in a substantial decrease in cognitive trust. The findings indicate that building cognitive trust in human-
robot interaction may be more complex than previously believed, involving emotional processes that are typically overlooked. We highlight an affective compensatory mechanism that adds a layer to consider alongside traditional competence-based models of cognitive trust.
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All authors Adi Manor, Dan Cohen, Ziv Keidar, Avi Parush, Hadas Erel
Robotics
Masked Generative Policy for Robotic Control
Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet et al. (+4 more)
We present Masked Generative Policy (MGP), a novel framework for visuomotor imitation learning. We represent actions as discrete
tokens, and train a conditional masked
transformer that generates
tokens in parallel and then rapidly refines only low-confidence
tokens. We further propose two new sampling paradigms: MGP-Short, which performs parallel masked generation with score-based refinement for Markovian tasks, and MGP-Long, which predicts full trajectories in a single pass and dynamically refines low-confidence action
tokens based on new observations. With globally coherent prediction and robust adaptive execution capabilities, MGP-Long enables reliable control on complex and non-Markovian tasks that prior methods struggle with. Extensive evaluations on 150 robotic manipulation tasks spanning the Meta-World and LIBERO
benchmarks show that MGP achieves both rapid inference and superior success rates compared to state-of-the-art
diffusion and autoregressive policies. Specifically, MGP increases the average success rate by 9% across 150 tasks while cutting per-sequence inference time by up to 35x. It further improves the average success rate by 60% in dynamic and missing-observation environments, and solves two non-Markovian scenarios where other state-of-the-art methods fail.
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All authors Lipeng Zhuang, Shiyu Fan, Florent P. Audonnet, Yingdong Ru, Edmond S. L. Ho, Gerardo Aragon Camarasa, Paul Henderson
Robotics · Cross-listed from eess.SY
Characterizing Human Feedback-Based Control in Naturalistic Driving Interactions via Gaussian Process Regression with Linear Feedback
Rachel DiPirro, Rosalyn Devonport, Dan Calderone et al. (+3 more)
Understanding driver interactions is critical to designing autonomous vehicles to interoperate safely with human-driven cars. We consider the impact of these interactions on the policies drivers employ when navigating unsigned intersections in a driving simulator. The simulator allows the collection of naturalistic decision-making and behavior data in a controlled environment. Using these data, we model the human driver responses as state-based feedback controllers learned via Gaussian Process regression methods. We compute the feedback gain of the controller using a weighted combination of linear and nonlinear priors. We then analyze how the individual gains are reflected in driver behavior. We also assess differences in these controllers across populations of drivers. Our work in data-driven analyses of how drivers determine their policies can facilitate future work in the design of socially responsive autonomy for vehicles.
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All authors Rachel DiPirro, Rosalyn Devonport, Dan Calderone, Chishang "Mario” Yang, Wendy Ju, Meeko Oishi
Robotics
Inferring Operator Emotions from a Motion-Controlled Robotic Arm
Xinyu Qi, Zeyu Deng, Shaun Alexander Macdonald et al. (+4 more)
A remote
robot operator's affective state can significantly impact the resulting
robot's motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator's affective states in remote
robot control scenarios is, however, underexplored. Current
emotion recognition methods rely on reading the user's vital signs or body
language, but the devices and user participation these measures require would add limitations to remote
robot control. We demonstrate that the functional movements of a remote-controlled robotic avatar, which was not designed for emotional expression, can be used to infer the emotional state of the human operator via a machine-learning system. Specifically, our system achieved 83.3$\%$ accuracy in recognizing the user's emotional state expressed by
robot movements, as a result of their hand motions. We discuss the implications of this system on prominent current and future remote
robot operation and affective robotic contexts.
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All authors Xinyu Qi, Zeyu Deng, Shaun Alexander Macdonald, Liying Li, Chen Wang, Muhammad Ali Imran, Philip G. Zhao
Robotics · Cross-listed from cs.CV
Adaptive Thresholding for Visual Place Recognition using Negative Gaussian Mixture Statistics
Nick Trinh and Damian Lyons
Visual place recognition (VPR) is an important component technology for camera-based mapping and
navigation applications. This is a challenging problem because images of the same place may appear quite different for reasons including seasonal changes, weather illumination, structural changes to the environment, as well as transient pedestrian or vehicle traffic. Papers focusing on generating image descriptors for VPR report their results using metrics such as recall@K and ROC curves. However, for a
robot implementation, determining which matches are sufficiently good is often reduced to a manually set threshold. And it is difficult to manually select a threshold that will work for a variety of visual scenarios. This paper addresses the problem of automatically selecting a threshold for VPR by looking at the 'negative' Gaussian mixture statistics for a place - image statistics indicating not this place. We show that this approach can be used to select thresholds that work well for a variety of image databases and image descriptors.
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Robotics
ShelfAware: Real-Time Semantic Localization in Quasi-Static Environments with Low-Cost Sensors
Shivendra Agrawal, Jake Brawer, Ashutosh Naik et al. (+2 more)
Many indoor workspaces are quasi-static: their global
geometric layout is stable, but local semantics change continually, producing repetitive geometry, dynamic clutter, and perceptual noise that defeat standard
vision-based localization. We present ShelfAware, a semantic particle filter for robust global localization that treats scene semantics as statistical evidence over object categories rather than fixed quantity landmarks. ShelfAware fuses a depth likelihood with a category-centric semantic similarity and uses a precomputed bank of semantic viewpoints to perform inverse semantic proposals inside Monte Carlo Localization (MCL), yielding fast, targeted hypothesis generation on low-cost,
vision-only hardware. To demonstrate perception-agnostic scalability, we evaluate ShelfAware across two domains. In a rigorously controlled mock retail environment, ShelfAware achieves a 97% global localization success rate, maintaining the highest tracking success (66%) across cart, wearable, and dynamic occlusion conditions. Furthermore, in a 3,500 sq. ft. operational grocery store leveraging an open-vocabulary
vision pipeline, ShelfAware significantly outperforms both
geometric and fixed-quantity semantic baselines. By modeling semantics distributionally and leveraging inverse proposals, ShelfAware resolves
geometric aliasing, providing an infrastructure-free building block for mobile and assistive
robots in dynamic real-world environments.
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All authors Shivendra Agrawal, Jake Brawer, Ashutosh Naik, Alessandro Roncone, Bradley Hayes
Robotics
OSMO: Open-Source Tactile Glove for Human-to-Robot Skill Transfer
Jessica Yin, Haozhi Qi, Youngsun Wi et al. (+7 more)
Human
video demonstrations provide abundant training data for learning
robot policies, but
video alone cannot capture the rich contact signals critical for mastering manipulation. We introduce OSMO, an open-source wearable tactile glove designed for human-to-
robot skill transfer. The glove features 12 three-axis tactile sensors across the fingertips and palm and is designed to be compatible with state-of-the-art hand-tracking methods for in-the-wild data collection. We demonstrate that a
robot policy trained exclusively on human demonstrations collected with OSMO, without any real
robot data, is capable of executing a challenging contact-rich manipulation task. By equipping both the human and the
robot with the same glove, OSMO minimizes the visual and tactile embodiment gap, enabling the transfer of continuous shear and normal force feedback while avoiding the need for image inpainting or other
vision-based force inference. On a real-world wiping task requiring sustained contact pressure, our tactile-aware policy achieves a 72% success rate, outperforming
vision-only baselines by eliminating contact-related failure modes. We release complete hardware designs, firmware, and assembly instructions to support community adoption.
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All authors Jessica Yin, Haozhi Qi, Youngsun Wi, Sayantan Kundu, Mike Lambeta, William Yang, Changhao Wang, Tingfan Wu, Jitendra Malik, Tess Hellebrekers
Robotics · Cross-listed from cs.CV
LiDAS: Lighting-driven Dynamic Active Sensing for Nighttime Perception
Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi et al. (+1 more)
Nighttime environments pose significant challenges for camera-based perception, as existing methods passively rely on the scene lighting. We introduce Lighting-driven Dynamic Active Sensing (LiDAS), a closed-loop active illumination system that combines off-the-shelf visual perception models with high-definition headlights. Rather than uniformly brightening the scene, LiDAS dynamically predicts an optimal illumination field that maximizes downstream perception performance, i.e., decreasing light on empty areas to reallocate it on object regions. LiDAS enables zero-shot nighttime generalization of daytime-trained models through adaptive illumination control. Trained on synthetic data and deployed zero-shot in real-world closed-loop driving scenarios, LiDAS enables +18.7% mAP50 and +5.0% mIoU over standard low-beam at equal power. It maintains performances while reducing energy use by 40%. LiDAS complements domain-generalization methods, further strengthening robustness without retraining. By turning readily available headlights into active
vision actuators, LiDAS offers a cost-effective solution to robust nighttime perception.
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All authors Simon de Moreau, Andrei Bursuc, Hafid El-Idrissi, Fabien Moutarde
Robotics · Cross-listed from cs.CV
Accelerated Rotation-Invariant Convolution for UAV Image Segmentation
Manduhu Manduhu, Alexander Dow, Gerard Dooly et al. (+1 more)
Rotation invariance is essential for precise, object-level
segmentation in
UAV aerial imagery, where targets can have arbitrary orientations and exhibit fine-scale details. Conventional
segmentation architectures like U-Net rely on convolution operators that are not rotation-invariant, leading to degraded
segmentation accuracy across varying viewpoints. Rotation invariance can be achieved by expanding the filter bank across multiple orientations; however, this will significantly increase computational cost and
memory traffic. In this paper, we introduce a GPU-optimized rotation-invariant convolution framework that eliminates the traditional data-lowering (im2col) step required for
matrix-multiplication-based convolution. By exploiting structured data sharing among symmetrically rotated filters, our method achieves multi-orientation convolution with greatly reduced
memory traffic and computational redundancy. We further generalize the approach to accelerate convolution with arbitrary (non-symmetric) rotation angles. Across extensive
benchmarks, the proposed convolution achieves 20–55% faster training and 15–45% lower energy consumption than CUDNN, while maintaining accuracy comparable to state-of-the-art rotation-invariant methods. In the eight-orientation setting, our approach achieves up to 45% speedup and 41% energy savings on 256\(\times\)256 inputs, and 32% speedup and 23% lower energy usage on 1024\(\times\)1024 inputs. Integrated into a U-Net
segmentation model, the framework yields up to 6% improvement in accuracy over the non-rotation-aware baseline. These results demonstrate that the proposed method provides an effective and highly efficient alternative to existing rotation-invariant CNN frameworks.
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All authors Manduhu Manduhu, Alexander Dow, Gerard Dooly, James Riordan
Robotics
IPPO Learns the Game, Not the Team: A Study on Generalization in Heterogeneous Agent Teams
Ryan LeRoy and Jack Kolb
Multi-
Agent Reinforcement Learning (MARL) is commonly deployed in settings where
agents are trained via self-play with homogeneous teammates, often using parameter sharing and a single policy architecture. This opens the question: to what extent do self-play PPO
agents learn general coordination strategies grounded in the underlying game, compared to overfitting to their training partners' behaviors? This paper investigates the question using the Heterogeneous Multi-
Agent Challenge (HeMAC) environment, which features distinct Observer and Drone
agents with complementary capabilities. We introduce Rotating Policy Training (RPT), an approach that rotates heterogeneous teammate policies of different learning
algorithms during training, to expose the
agent to a broader range of partner strategies. When playing alongside a withheld teammate policy (DDQN), we find that RPT achieves similar performance to a standard self-play baseline, IPPO, where all
agents were trained sharing a single PPO policy. This result indicates that in this heterogeneous multi-
agent setting, the IPPO baseline generalizes to novel teammate
algorithms despite not experiencing teammate diversity during training. This shows that a simple IPPO baseline may possess the level of generalization to novel teammates that a diverse training regimen was designed to achieve.
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Robotics
Heterogeneity in Multi-Robot Environmental Monitoring for Resolving Time-Conflicting Tasks
Connor York, Zachary R Madin, Paul O'Dowd et al. (+1 more)
Multi-
robot systems performing continuous tasks face a performance trade-off when interrupted by urgent, time-critical sub-tasks. We investigate this trade-off in a scenario where a team must balance area patrolling with locating an anomalous
radio signal. To address this trade-off, we evaluate both behavioral heterogeneity through
agent role specialization ("patrollers" and "searchers") and sensing heterogeneity (i.e., only the searchers can sense the
radio signal). Through simulation, we identify the Pareto-optimal trade-offs under varying team compositions, with behaviorally heterogeneous teams demonstrating the most balanced trade-offs in the majority of cases. When sensing capability is restricted, heterogeneous teams with half of the sensing-capable
agents perform comparably to homogeneous teams, providing cost-saving rationale for restricting sensor payload deployment. Our findings demonstrate that pre-deployment role and sensing specialization are powerful design considerations for multi-
robot systems facing time-conflicting tasks, where varying the degree of behavioral heterogeneity can tune system performance toward either task.
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All authors Connor York, Zachary R Madin, Paul O'Dowd, Edmund R Hunt
Robotics
Data-Driven Dynamic Parameter Learning of manipulator robots
Mohammed Elseiagy, Tsige Tadesse Alemayoh, Ranulfo Bezerra et al. (+2 more)
Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex
robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a
Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse
robot models and enriched trajectory data using Jacobian-derived features. The
dataset consists of 8,192
robots with varied inertial and frictional properties. Leveraging
attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining
Transformers with automated
dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems
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All authors Mohammed Elseiagy, Tsige Tadesse Alemayoh, Ranulfo Bezerra, Shotaro Kojima, Kazunori Ohno
Robotics
A Multi-Robot Platform for Robotic Triage Combining Onboard Sensing and Foundation Models
Jason Hughes, Marcel Hussing, Edward Zhang et al. (+24 more)
This report presents a heterogeneous robotic system designed for remote primary triage in mass-casualty incidents (MCIs). The system employs a coordinated air-ground team of unmanned aerial vehicles (
UAVs) and unmanned ground vehicles (UGVs) to locate victims, assess their injuries, and prioritize medical assistance without risking the lives of first responders. The
UAV identify and provide overhead views of casualties, while UGVs equipped with specialized sensors measure vital signs and detect and localize physical injuries. Unlike previous work that focused on exploration or limited medical evaluation, this system addresses the complete triage process: victim localization, vital sign measurement, injury severity classification, mental status assessment, and data consolidation for first responders. Developed as part of the DARPA Triage Challenge, this approach demonstrates how multi-
robot systems can augment human capabilities in disaster response scenarios to maximize lives saved.
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All authors Jason Hughes, Marcel Hussing, Edward Zhang, Shenbagaraj Kannapiran, Joshua Caswell, Kenneth Chaney, Ruichen Deng, Michaela Feehery, Agelos Kratimenos, Yi Fan Li, Britny Major, Ethan Sanchez, Sumukh Shrote, Youkang Wang, Jeremy Wang, Daudi Zein, Luying Zhang, Ruijun Zhang, Alex Zhou, Tenzi Zhouga, Jeremy Cannon, Zaffir Qasim, Jay Yelon, Fernando Cladera, Kostas Daniilidis, Camillo J. Taylor, Eric Eaton
Robotics
Non Normalized Shared-Constraint Dynamic Games for Human-Robot Collaboration with Asymmetric Responsibility
Mark Pustilnik and Francesco Borrelli
This paper proposes a dynamic game formulation for cooperative human-
robot navigation in shared workspaces with obstacles, where the human and
robot jointly satisfy shared safety constraints while pursuing a common task. A key contribution is the introduction of a non-normalized
equilibrium structure for the shared constraints. This structure allows the two
agents to contribute different levels of effort towards enforcing safety requirements such as collision avoidance and inter-players spacing. We embed this non-normalized
equilibrium into a receding-horizon optimal control scheme.
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Robotics
Ergodic Trajectory Planning with Dynamic Sensor Footprints
Ziyue Zheng, Yongce Liu, Hesheng Wang et al. (+1 more)
This paper addresses the problem of trajectory planning for information gathering with a dynamic and resolution-varying sensor footprint. Ergodic planning offers a principled framework that balances exploration (visiting all areas) and exploitation (focusing on high-information regions) by planning trajectories such that the time spent in a region is proportional to the amount of information in that region. Existing ergodic planning often oversimplifies the sensing model by assuming a point sensor or a footprint with constant shape and resolution. In practice, the sensor footprint can drastically change over time as the
robot moves, such as aerial
robots equipped with downward-facing cameras, whose field of view depends on the orientation and altitude. To overcome this limitation, we propose a new metric that accounts for dynamic sensor footprints, analyze the theoretic local optimality conditions, and propose numerical trajectory optimization
algorithms. Experimental results show that the proposed approach can simultaneously optimize both the trajectories and sensor footprints, with up to an order of magnitude better ergodicity than conventional methods. We also deploy our approach in a multi-drone system to ergodically cover an object in 3D space.
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All authors Ziyue Zheng, Yongce Liu, Hesheng Wang, Zhongqiang Ren
Robotics
Sim2Swim: Zero-Shot Velocity Control for Agile AUV Maneuvering in 3 Minutes
Lauritz Rismark Fosso, Herman Biørn Amundsen, Marios Xanthidis et al. (+1 more)
Holonomic autonomous
underwater vehicles (AUVs) have the hardware ability for agile maneuvering in both translational and rotational degrees of freedom (DOFs). However, due to challenges inherent to
underwater vehicles, such as complex hydrostatics and hydrodynamics, parametric uncertainties, and frequent changes in dynamics due to payload changes, control is challenging. Performance typically relies on carefully tuned controllers targeting unique platform configurations, and a need for re-tuning for deployment under varying payloads and hydrodynamic conditions. As a consequence, agile maneuvering with simultaneous tracking of time-varying references in both translational and rotational DOFs is rarely utilized in practice. To the best of our knowledge, this paper presents the first general zero-shot sim2real deep reinforcement learning-based (DRL) velocity controller enabling path following and agile 6DOF maneuvering with a training duration of just 3 minutes. Sim2Swim, the proposed approach, inspired by state-of-the-art DRL-based position control, leverages domain randomization and massively parallelized training to converge to field-deployable control policies for AUVs of variable characteristics without post-processing or tuning. Sim2Swim is extensively validated in pool trials for a variety of configurations, showcasing robust control for highly agile motions.
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All authors Lauritz Rismark Fosso, Herman Biørn Amundsen, Marios Xanthidis, Sveinung Johan Ohrem
Robotics
A Sensor-Aware Phenomenological Framework for Lidar Degradation Simulation and SLAM Robustness Evaluation
Doumegna Mawuto Koudjo Felix, Xianjia Yu, Zhuo Zou et al. (+1 more)
Lidar-based SLAM systems are highly sensitive to adverse conditions such as occlusion, noise, and field-of-view (FoV) degradation, yet existing robustness evaluation methods either lack physical grounding or do not capture sensor-specific behavior. This paper presents a sensor-aware, phenomenological framework for simulating interpretable
lidar degradations directly on real point clouds, enabling controlled and reproducible SLAM stress testing. Unlike image-derived corruption
benchmarks (e.g., SemanticKITTI-C) or simulation-only approaches (e.g., lidarsim), the proposed system preserves per-point geometry, intensity, and temporal structure while applying structured dropout, FoV reduction, Gaussian noise, occlusion masking, sparsification, and motion distortion. The framework features autonomous topic and sensor detection, modular configuration with four severity tiers (light–extreme), and real-time performance (less than 20 ms per frame) compatible with ROS workflows. Experimental validation across three
lidar architectures and five state-of-the-art SLAM systems reveals distinct robustness patterns shaped by sensor design and environmental context. The open-source implementation provides a practical foundation for benchmarking
lidar-based SLAM under physically meaningful degradation scenarios.
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All authors Doumegna Mawuto Koudjo Felix, Xianjia Yu, Zhuo Zou, Tomi Westerlund
Robotics
Multi-Task Bayesian Optimization for Tuning Decentralized Trajectory Generation in Multi-UAV Systems
Marta Manzoni, Alessandro Nazzari, Roberto Rubinacci et al. (+1 more)
This paper investigates the use of Multi-Task
Bayesian Optimization for tuning decentralized trajectory generation
algorithms in multi-drone systems. We treat each task as a trajectory generation scenario defined by a specific number of drone-to-drone interactions. To model relationships across scenarios, we employ Multi-Task Gaussian Processes, which capture shared structure across tasks and enable efficient information transfer during optimization. We compare two strategies: optimizing the average mission time across all tasks and optimizing each task individually. Through a comprehensive simulation campaign, we show that single-task optimization leads to progressively shorter mission times as swarm size grows, but requires significantly more optimization time than the average-task approach.
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All authors Marta Manzoni, Alessandro Nazzari, Roberto Rubinacci, Marco Lovera
Robotics · Cross-listed from eess.SY
Decoupled Design of Time-Varying Control Barrier Functions via Equivariances
Adrian Wiltz and Dimos V. Dimarogonas
This article presents a systematic method for designing time-varying Control Barrier Functions (CBF) composed of a time-invariant component and multiple time-dependent components, leveraging structural properties of the system dynamics. The method involves the construction of a specific class of time-invariant CBFs that encode the system's dynamic capabilities with respect to a given constraint, and augments them subsequently with appropriately designed time-dependent transformations. While transformations uniformly varying the time-invariant CBF can be applied to arbitrary systems, transformations exploiting structural properties in the dynamics - equivariances in particular - enable the handling of a broader and more expressive class of time-varying constraints. The article shows how to leverage such properties in the design of time-varying CBFs. The proposed method decouples the design of time variations from the computationally expensive construction of the underlying CBFs, thereby providing a computationally attractive method to the design of time-varying CBFs. The method accounts for input constraints and under-actuation, and requires only qualitative knowledge on the time-variation of the constraints making it suitable to the application in uncertain environments.
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