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A Unified Framework for Robots that Influence Humans over Long-Term Interaction

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arXiv 2025年

作者： Sagheb, Shahabedin Parekh, Sagar Pandya, Ravi Mun, Ye-Ji Driggs-Campbell, Katherine Bajcsy, Andrea Losey, Dylan P. Virginia Tech Department of Mechanical Engineering United States Carnegie Melon University Robotics Institute United States University of Illinois Urbana-Champaign Department of Electrical and Computer Engineering United States

Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human’s behavior in a way that helps the robot complete its task. Imagine an autonomous car trying to merge;by proactively nudging into the human’s lane, the robot causes human drivers to yield and provide space. Influence is often necessary for seamless interaction. However, if influence is left unregulated and uncontrolled, robots will negatively impact the humans around them (e.g., autonomous cars that repeatedly merge in front of humans may cause human drivers to become more aggressive). Prior works have begun to address this problem by creating a variety of control algorithms that seek to influence humans. Although these methods are effective in the short-term, they fail to maintain influence over time as the human adapts to the robot’s behaviors. In this paper we therefore present an optimization framework that enables robots to purposely regulate their influence over humans across both short-term and long-term interactions. Here the robot maintains its influence by reasoning over a dynamic human model which captures how the robot’s current choices will impact the human’s future behavior. Our resulting framework serves to unify current approaches: we demonstrate that state-of-the-art methods are simplifications of our underlying formalism. Our framework also provides a principled way to generate influential policies: in the best case the robot exactly solves our framework to find optimal, influential behavior. But when solving this optimization problem becomes impractical, designers can introduce their own simplifications to reach tractable approximations. We experimentally compare our unified framework to state-of-the-art baselines and ablations, and demonstrate across simulations and user studies that this framework is able to successfully influence humans over repeated interactions. See videos of our experiments he

关键词： Human robot interaction

From Vague Instructions to Task Plans: A Feedback-Driven HRC Task Planning Framework based on LLMs

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arXiv 2025年

作者： Shervedani, Afagh Mehri Walter, Matthew R. Žefran, Miloš Robotics Laboratory Department of Electrical and Computer Engineering University of Illinois Chicago ChicagoIL60607 United States Robotics Laboratory Toyota Technological Institute at Chicago ChicagoIL60637 United States

Recent advances in large language models (LLMs) have demonstrated their potential as planners in human-robot collaboration (HRC) scenarios, offering a promising alternative to traditional planning methods. LLMs, which can generate structured plans by reasoning over natural language inputs, have the ability to generalize across diverse tasks and adapt to human instructions. This paper investigates the potential of LLMs to facilitate planning in the context of human-robot collaborative tasks, with a focus on their ability to reason from high-level, vague human inputs, and fine-tune plans based on real-time feedback. We propose a novel hybrid framework that combines LLMs with human feedback to create dynamic, context-aware task plans. Our work also highlights how a single, concise prompt can be used for a wide range of tasks and environments, overcoming the limitations of long, detailed structured prompts typically used in prior studies. By integrating user preferences into the planning loop, we ensure that the generated plans are not only effective but aligned with human intentions. Copyright © 2025, The Authors. All rights reserved.

关键词： Robot programming

Identification of Medicinal Plants and Related Fungal Diseases Based on Deep Learning Model

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Identification of Medicinal Plants and Related Fungal Diseas...

Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Mathina Kani M Mahalakshmi R Shunmugavel V Abdul Kareem M Department of Computer Science and Engineering Sethu Institute of Technology Virudhunagar India Department of Computer Science and Business Systems Sethu Institute of Technology Virudhunagar India Department of Electrical and Electronics Engineering Quantanics TechServ pvt Ltd Madurai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

In today's technological landscape, manual methods are increasingly being replaced by automated computer systems, streamlining tasks and enhancing efficiency. Within the realm of agricultural research, two critical areas plant identification and disease classification have shifted toward computerized solutions. With millions of plant species globally, medicinal plants hold particular significance due to their healing properties in traditional medicine. These readily accessible and culturally important remedies not only contribute to affordable healthcare but also sustain livelihoods in indigenous and rural communities. To address these challenges, our study evaluates the performance of two widely used pre-trained deep learning models U-Net and F-Net. By comparing their accuracy levels, we aim to enhance disease detection. Additionally, we present performance metrics such as accuracy and error rates, visualized through graphs. Our user interface (UI) leverages Kivy, an open-source multi-platform GUI development library for Python. Kivy enables cross-device app development, emphasizing code reusability and rapid prototyping, making it a valuable tool for interaction design across iOS, Android, Windows, OS X, and GNU/Linux platforms.

关键词： Medicinal plants Deep learning Measurement Visualization Accuracy Medical services Manuals Rapid prototyping Mobile computing Diseases

The Utilization of a Parallel Computing Algorithm for Accelerating Switching-Level Modeling of Power Electronics Simulations in a T-Type PV Inverter

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The Utilization of a Parallel Computing Algorithm for Accele...

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Buck F. Brown Liwei Wang Zheyu Zhang Johan Enslin Yi Li Department of Electrical and Computer Engineering Clemson University North Charleston SC USA School of Computing Clemson University North Charleston SC USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

High-fidelity, switching-level modeling of power electronics simulations can be computationally intensive and time-consuming. This computational burden has recently escalated further due to the increased number of converters implemented in a system, e.g., renewable energy systems. Unlike switching modeling, average modeling can alleviate the computational burden and adequately represent a converter’s behavior for controller design. However, average modeling does not encapsulate switching harmonics, which are crucial for power electronics design, operation, and reliability studies. This paper proposes a novel way to accelerate the switching-level modeling of simulations by leveraging parallel computing techniques and showcases this technique in a specific case study. This new technique is called the average-to-switching (A2S) model. First, the basic concept of the A2S model is outlined. Second, a case study is designed for a two-stage, T-type PV inverter. Third, electrical and thermal waveforms produced by the case study are given and discussed. The results of testing the A2S model showcase a speedup of greater than 22 times the simulation benchmark while maintaining an R 2 value of greater than 0.99.

关键词： Renewable energy sources Electric potential Accuracy Computational modeling Switches Parallel processing Power system harmonics Reliability engineering Multilevel inverters Harmonic analysis

Verifying Nonlinear Neural Feedback systems using Polyhedral Enclosures

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arXiv 2025年

作者： Akinwande, Samuel I. Sidrane, Chelsea Kochenderfer, Mykel J. Barrett, Clark Department of Aeronautical and Astronautical Engineering Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Division of Robotics Perception and Learning Intelligent Systems Department School of Electrical Engineering & Computer Science KTH Royal Institute of Technology Stockholm Sweden

As dynamical systems equipped with neural network controllers (neural feedback systems) become increasingly prevalent, it is critical to develop methods to ensure their safe operation. Verifying safety requires extending control theoretic analysis methods to these systems. Although existing techniques can efficiently handle linear neural feedback systems, relatively few scalable methods address the nonlinear case. We propose a novel algorithm for forward reachability analysis of nonlinear neural feedback systems. The approach leverages the structure of the nonlinear transition functions of the systems to compute tight polyhedral enclosures (i.e., abstractions). These enclosures, combined with the neural controller, are then encoded as a mixed-integer linear program (MILP). Optimizing this MILP yields a sound over-approximation of the forward-reachable set. We evaluate our algorithm on representative benchmarks and demonstrate an order of magnitude improvement over the current state of the art. Copyright © 2025, The Authors. All rights reserved.

关键词： Mixed-integer linear programming

A distributed algorithm with network-independent step-size and event-triggered mechanism for economic dispatch problem

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A distributed algorithm with network-independent step-size a...

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, Witold Sun, Changhai School of Control Science and Engineering Dalian University of Technology Dalian China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada Institute of Systems Engineering Macau University of Science and Technology SARTaipa 999078 China Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey School of Electrical Engineering Dalian University of Technology Dalian China

The economic dispatch problem (EDP) poses a significant challenge in energy management for modern power systems, particularly as these systems undergo expansion. This growth escalates the demand for communication resources and increases the risk of communication failures. To address this challenge, we propose a distributed algorithm with network-independent step sizes and an event-triggered mechanism, which reduces communication requirements and enhances adaptability. Unlike traditional methods, our algorithm uses network-independent step sizes derived from each agent's local cost functions, thus eliminating the need for detailed network topology knowledge. The theoretical derivation identifies a range of step size values that depend solely on the objective function's strong convexity and the gradient's Lipschitz continuity. Furthermore, the proposed algorithm is shown to achieve a linear convergence rate, assuming the event triggering threshold criteria are met for linear convergence. Numerical experiments further validate the effectiveness and advantages of our proposed distributed algorithm by demonstrating its ability to maintain good convergence characteristics while reducing communication frequency. © 2024 John Wiley & Sons Ltd.

关键词： Cost functions

Distributed Prescribed-Time Observer for Nonlinear systems in Block-Triangular Form

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IEEE Control systems Letters 2025年 9卷 222-227页

作者： Vincent de Heij M. Umar B. Niazi Karl H. Johansson Saeed Ahmed Jan C. Willems Center for Systems and Control and the Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen Groningen The Netherlands Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA

This letter proposes a design of a distributed prescribed-time observer for nonlinear systems representable in a block-triangular observable canonical form. Using a weighted average of neighbor estimates exchanged over a strongly connected digraph, each observer estimates the system state despite the limited observability of local sensor measurements. The proposed design guarantees that distributed state estimation errors converge to zero at a user-specified convergence time, irrespective of observers’ initial conditions. To achieve this prescribed-time convergence, distributed observers implement time-varying local output injection gains that monotonically increase and approach infinity at the prescribed time. The theoretical convergence is rigorously proven and validated through numerical simulations, where some implementation issues due to increasing gains have also been clarified.

关键词： Observers Convergence Nonlinear systems Symmetric matrices Observability Directed graphs Control systems Vectors Linear systems Electronic mail

Single-Stage 48V-to-1V Regulator with a Half-Turn Transformer and Current-Doubler Rectifier

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Single-Stage 48V-to-1V Regulator with a Half-Turn Transforme...

Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Xinmiao Xu Qiang Li Department of Electrical and Computer Engineering Center for Power Electronics Systems Virginia Polytechnic Institute and State University Blacksburg VA United States

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Single-stage 48V-to-1V regulated solutions for high-performance processors become increasingly popular. This paper presents a full-bridge transformer-based buck converter with the current-doubler rectifier. The transformer adopts a half-turn winding structure for reduced resistance and leakage inductance. Operating principles of the converter with the half-turn transformer and the converter’s optimized printed circuit board layout are illustrated. Synchronous rectifiers are integrated in high-frequency loops and are treated carefully to reduce parasitic-inductor-induced loss. With the help of recent advances in gallium nitride (GaN) and silicon transistors, a 48V-to-1.8V GaN-based prototype switching at 700 kHz is constructed, which achieves 95.34% peak efficiency, 92.79% full-load efficiency (including gating loss) and 576 W/in 3 power density with 50-A current per phase. The same hardware achieves 94.00% peak efficiency and 89.75% full-load efficiency with 55-A current per phase in 48V-to-1.0V conversion. Experimental results with 54-V input are also included in this paper, demonstrating the great potential of the single-stage solution for future data centers.

关键词： Inductance Regulators Switching frequency Windings Layout Printed circuits Rectifiers Prototypes Transformers Gallium nitride

Whole-Body Model-Predictive Control of Legged Robots with MuJoCo

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arXiv 2025年

作者： Zhang, John Z. Howell, Taylor A. Yi, Zeji Pan, Chaoyi Shi, Guanya Qu, Guannan Erez, Tom Tassa, Yuval Manchester, Zachary Robotics Institute Carnegie Mellon University United States Google Deep-Mind United Kingdom Department of Electrical and Computer Engineering Carnegie Mellon University United States

We demonstrate the surprising real-world effectiveness of a very simple approach to whole-body model-predictive control (MPC) of quadruped and humanoid robots: the iterative LQR (iLQR) algorithm with MuJoCo dynamics and finite-difference approximated derivatives. Building upon the previous success of model-based behavior synthesis and control of locomotion and manipulation tasks with MuJoCo in simulation, we show that these policies can easily generalize to the real world with few sim-to-real considerations. Our baseline method achieves real-time whole-body MPC on a variety of hardware experiments, including dynamic quadruped locomotion, quadruped walking on two legs, and full-sized humanoid bipedal locomotion. We hope this easy-to-reproduce hardware baseline lowers the barrier to entry for real-world whole-body MPC research and contributes to accelerating research velocity in the community. Our code and experiment videos will be available online at: https://***/mujoco ilqr. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive control systems