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University of Texas at Arlington

Academic institutionnorthamerica · us
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Research library278linked papers
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Selected work

Representative Papers

Movement Primitives in Robotics: A Comprehensive Survey

Dec 17, 2025arXiv.org

This paper presents a systematic review of movement primitive approaches in robot control, with a focus on learning from human demonstrations to generate complex action sequences. Integrating chronological and systematic perspectives, it comprehensively traces the theoretical evolution of movement primitives, key technical advances—including spring-damper modeling, probabilistic coupling of multiple demonstration trajectories, and neural network applications in high-dimensional systems—and their empirical effectiveness in tasks such as grasping and throwing. The study offers an in-depth comparative analysis of prevailing frameworks, establishes for the first time a structured developmental trajectory of the field, and clearly identifies current open challenges and practical limitations, thereby providing both theoretical guidance and a practical roadmap for research in robotic motor skill learning.

2 citationsRead paper

Secret sharing with additive access structures from correlated random variables

Jan 14, 2026

This work investigates efficient secret sharing under dynamically growing additive access structures, leveraging correlated randomness and public communication. Specifically, it addresses scenarios where the access structure expands monotonically over time, any subset of participants may join at any moment, and the dealer learns of structural changes only when they occur. The paper presents the first extension of the correlated-randomness-based secret sharing model to such a dynamic setting. The proposed scheme achieves, at every step, the optimal secret rate attainable in the static model and meets the information-theoretic capacity for threshold access structures. This significantly broadens the applicability of existing theoretical frameworks in secret sharing.

1 citationsRead paper

Secret-Key Generation from Private Identifiers under Channel Uncertainty

Mar 11, 2025

This work addresses physical-layer key generation for device authentication under unknown channel statistics. We propose a privacy-enhancing key agreement framework leveraging physical unclonable functions (PUFs). Considering a multi-antenna receiver and an eavesdropper with access to auxiliary side information, we formulate a three-party information-theoretic model comprising encoder, legitimate decoder, and eavesdropper. For general discrete memoryless sources, we derive the first tight inner and outer bounds on the optimal trade-off among secret key rate, storage rate, and privacy leakage rate; these bounds are proven to be tight for Gaussian sources. Departing from the conventional assumption of perfect channel-state knowledge, our analysis establishes the achievable key capacity region in uncertain wireless environments. The results provide a rigorous information-theoretic benchmark and principled design guidelines for low-overhead, high-security authentication of IoT devices.

1 citationsRead paper

Prioritizing Risk Factors in Media Entrepreneurship on Social Networks: Hybrid Fuzzy Z-Number Approaches for Strategic Budget Allocation and Risk Management in Advertising Construction Campaigns

Sep 13, 2024arXiv.org

This paper addresses the challenge of jointly optimizing advertising budget allocation and risk management in social media startups. To this end, it integrates Media Mix Modeling (MMM) with Failure Mode and Effects Analysis (FMEA), establishing a dynamic, risk-aware decision-making framework. Methodologically, it innovatively embeds Z-number theory into the FMEA process and proposes a hybrid approach—Z-SWARA for criterion weight determination and Z-WASPAS for multi-criteria ranking—overcoming the limitations of traditional Risk Priority Number (RPN) in modeling both fuzziness and reliability. The framework enables credibility-weighted assessment of risk factors across advertising channels. Empirical evaluation demonstrates a 12.7% improvement in ROI prediction accuracy and a 35% enhancement in risk response timeliness across three representative social media startup scenarios.

1 citationsRead paper

Safe Control and Learning Using the Generalized Action Governor

Nov 22, 2022

To address the challenge of simultaneously ensuring safety and optimizing performance in uncertain dynamical systems, this paper proposes a safety-critical learning framework based on generalized action governors (AGs). The framework unifies the modeling of diverse safety enforcement mechanisms for multiple system classes and rigorously integrates AGs with both reinforcement learning (RL) and Koopman operator-based control, guaranteeing strict satisfaction of state constraints throughout the entire learning process. Methodologically, it encompasses AG synthesis, constraint analysis for linear and discrete-time systems, safety-aware RL, data-driven Koopman model identification, and real-time feasibility verification. We provide theoretical guarantees on closed-loop stability and safety of the AG-augmented system. Numerical experiments demonstrate that the two proposed safe learning algorithms achieve significant improvements in closed-loop performance—without any constraint violations.

1 citationsRead paper
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