Tootfinder

Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Ersin Das, Rahal Nanayakkara, Xiao Tan, Ryan M. Bena, Joel W. Burdick, Paulo Tabuada, Aaron D. Ames
https://arxiv.org/abs/2508.19159

Safe Navigation under State Uncertainty: Online Adaptation for Robust Control Barrier Functions
Measurements and state estimates are often imperfect in control practice, posing challenges for safety-critical applications, where safety guarantees rely on accurate state information. In the presence of estimation errors, several prior robust control barrier function (R-CBF) formulations have imposed strict conditions on the input. These methods can be overly conservative and can introduce issues such as infeasibility, high control effort, etc. This work proposes a systematic method to improv…

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
Ryan Ghamandi, Yahya Hmaiti, Mykola Maslych, Ravi Kiran Kattoju, Joseph J. LaViola Jr
https://arxiv.org/abs/2508.17124

Towards Deeper Understanding of Natural User Interactions in Virtual Reality Based Assembly Tasks
We explore natural user interactions using a virtual reality simulation of a robot arm for assembly tasks. Using a Wizard-of-Oz study, participants completed collaborative LEGO and instructive PCB assembly tasks, with the robot responding under experimenter control. We collected voice, hand tracking, and gaze data from users. Statistical analyses revealed that instructive and collaborative scenarios elicit distinct behaviors and adopted strategies, particularly as tasks progress. Users tended t…

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Ryan M. Bena, Gilbert Bahati, Blake Werner, Ryan K. Cosner, Lizhi Yang, Aaron D. Ames
https://arxiv.org/abs/2508.11129

Geometry-Aware Predictive Safety Filters on Humanoids: From Poisson Safety Functions to CBF Constrained MPC
Autonomous navigation through unstructured and dynamically-changing environments is a complex task that continues to present many challenges for modern roboticists. In particular, legged robots typically possess manipulable asymmetric geometries which must be considered during safety-critical trajectory planning. This work proposes a predictive safety filter: a nonlinear model predictive control (MPC) algorithm for online trajectory generation with geometry-aware safety constraints based on con…

Separating Intent from Execution: A Probabilistic Approach to Pitch Location Accuracy
Matt Ludwig, Ryan S. Brill, Abraham J. Wyner
https://arxiv.org/abs/2508.19184 https://

Separating Intent from Execution: A Probabilistic Approach to Pitch Location Accuracy
Control has long been recognized as a critical component of pitcher performance, reflecting a pitcher's ability to execute pitches in alignment with his intended targets. However, accurately inferring a pitcher's intentions presents a persistent challenge. Traditional metrics typically rely on uniformity assumptions, inferring intent based on the behavior of a ``typical'' pitcher across similar situations. In this study, we propose an alternative, individualized approach to measuring control, o…

Matrix Control Barrier Functions
Pio Ong, Yicheng Xu, Ryan M. Bena, Faryar Jabbari, Aaron D. Ames
https://arxiv.org/abs/2508.11795 https://arxiv.org/pdf/25…

Matrix Control Barrier Functions
This paper generalizes the control barrier function framework by replacing scalar-valued functions with matrix-valued ones. Specifically, we develop barrier conditions for safe sets defined by matrix inequalities -- both semidefinite and indefinite. Matrix inequalities can be used to describe a richer class of safe sets, including nonsmooth ones. The safety filters constructed from our proposed matrix control barrier functions via semidefinite programming (CBF-SDP) are shown to be continuous. O…

In-domain SSL pre-training and streaming ASR
Jarod Duret, Salima Mdhaffar, Ga\"elle Laperri\`ere, Ryan Whetten, Audrey Galametz, Catherine Kobus, Marion-C\'ecile Martin, Jo Oleiwan, Yannick Est\`eve
https://arxiv.org/abs/2509.12101

In-domain SSL pre-training and streaming ASR
In this study, we investigate the benefits of domain-specific self-supervised pre-training for both offline and streaming ASR in Air Traffic Control (ATC) environments. We train BEST-RQ models on 4.5k hours of unlabeled ATC data, then fine-tune on a smaller supervised ATC set. To enable real-time processing, we propose using chunked attention and dynamic convolutions, ensuring low-latency inference. We compare these in-domain SSL models against state-of-the-art, general-purpose speech encoders …

Replaced article(s) found for cs.SD. https://arxiv.org/list/cs.SD/new
[1/1]:
- Adaptive Linearly Constrained Minimum Variance Framework for Volumetric Active Noise Control
Manan Mittal, Ryan M. Corey, Andrew C. Singer

Dimensionality reduction of optically generated vortex strings in a charge density wave
Sajal Dahal, Alex H. Miller, Viktor Krapivin, Gal Orenstein, Ryan A. Duncan, Nicholas Leonard, Matthew J. Hurley, Jade Stanton, Roman Mankowsky, Henrik Lemke, Anisha Singh, Ian Fisher, Mariano Trigo, Samuel W. Teitelbaum
https://arxiv.org/abs/2509.09819…

Dimensionality reduction of optically generated vortex strings in a charge density wave
In phase transitions, mesoscale structures such as topological defects, vortex strings, and domain walls control the path towards equilibrium, and thus the functional properties of many active devices. In photoinduced phase transitions driven by femtosecond laser excitation, the temporal (pulse duration) and spatial (penetration depth) structure of the optical excitation present opportunities for control and creating structures with unique topologies. By performing time-resolved optical pump, x…

Crosslisted article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Pol Mestres, Blake Werner, Ryan K. Cosner, Aaron D. Ames

Quantum algorithm for linear matrix equations
Rolando D. Somma, Guang Hao Low, Dominic W. Berry, Ryan Babbush
https://arxiv.org/abs/2508.02822 https://arxi…

Quantum algorithm for linear matrix equations
We describe an efficient quantum algorithm for solving the linear matrix equation AX+XB=C, where A, B and C are given complex matrices and X is unknown. This is known as the Sylvester equation, a fundamental equation with applications in control theory and physics. Rather than encoding the solution in a quantum state in a fashion analogous to prior quantum linear algebra solvers, our approach constructs the solution matrix X in a block-encoding, rescaled by some factor. This allows us to obtain…

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Ryan Warnick
https://arxiv.org/abs/2508.01988 https://

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Aleatoric and Epistemic uncertainty have achieved recent attention in the literature as different sources from which uncertainty can emerge in stochastic modeling. Epistemic being intrinsic or model based notions of uncertainty, and aleatoric being the uncertainty inherent in the data. We propose a novel decision theoretic framework for outlier detection in the context of aleatoric uncertainty; in the context of Bayesian modeling. The model incorporates bayesian false discovery rate control for…

Steering MoE LLMs via Expert (De)Activation
Mohsen Fayyaz, Ali Modarressi, Hanieh Deilamsalehy, Franck Dernoncourt, Ryan Rossi, Trung Bui, Hinrich Sch\"utze, Nanyun Peng
https://arxiv.org/abs/2509.09660

Steering MoE LLMs via Expert (De)Activation
Mixture-of-Experts (MoE) in Large Language Models (LLMs) routes each token through a subset of specialized Feed-Forward Networks (FFN), known as experts. We present SteerMoE, a framework for steering MoE models by detecting and controlling behavior-linked experts. Our detection method identifies experts with distinct activation patterns across paired inputs exhibiting contrasting behaviors. By selectively (de)activating such experts during inference, we control behaviors like faithfulness and s…

Autonomous Soft Robotic Guidewire Navigation via Imitation Learning
Noah Barnes, Ji Woong Kim, Lingyun Di, Hannah Qu, Anuruddha Bhattacharjee, Miroslaw Janowski, Dheeraj Gandhi, Bailey Felix, Shaopeng Jiang, Olivia Young, Mark Fuge, Ryan D. Sochol, Jeremy D. Brown, Axel Krieger
https://arxiv.org/abs/2510.09497

Autonomous Soft Robotic Guidewire Navigation via Imitation Learning
In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations. Guidewires with robotic tips can enhance maneuverability, but they present challenges in modeling and control. Automation of soft robotic guidewire navigation has the potential to overcome these challenges, increasing the precision and safety of endova…

Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Pol Mestres, Blake Werner, Ryan K. Cosner, Aaron D. Ames
https://arxiv.org/abs/2510.01501

Probabilistic Control Barrier Functions: Safety in Probability for Discrete-Time Stochastic Systems
Control systems operating in the real world face countless sources of unpredictable uncertainties. These random disturbances can render deterministic guarantees inapplicable and cause catastrophic safety failures. To overcome this, this paper proposes a method for designing safe controllers for discrete-time stochastic systems that retain probabilistic guarantees of safety. To do this we modify the traditional notion of a control barrier function (CBF) to explicitly account for these stochastic…