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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixing and Contrastive-Constrained Accelerated Training

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FlashMix: Fast Map-Free LiDAR Localization via Feature Mixin...

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Raktim Gautam Goswami Naman Patel Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) NYU Tandon School of Engineering Brooklyn NY USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Map-free LiDAR localization systems accurately localize within known environments by predicting sensor position and orientation directly from raw point clouds, eliminating the need for large maps and descriptors. However, their long training times hinder rapid adaptation to new environments. To address this, we propose FlashMix, which uses a frozen, scene-agnostic backbone to extract local point descriptors, aggregated with an MLP mixer to predict sensor pose. A buffer of local descriptors is used to accelerate training by orders of magnitude, combined with metric learning or contrastive loss regularization of aggregated descriptors to improve performance and convergence. We evaluate FlashMix on various LiDAR localization benchmarks, examining different regularizations and aggregators, and demonstrating its effectiveness for rapid and accurate LiDAR localization in real-world scenarios. The code is available at https://***/raktimgg/FlashMix.

关键词： Location awareness Training Measurement Point cloud compression Laser radar Accuracy Pose estimation Benchmark testing Mixers Convergence

Energy Minimization using Custom-Designed Magnetic-Spring Actuators

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Energy Minimization using Custom-Designed Magnetic-Spring Ac...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Yue Yang Fu Ali U. Kilic David J. Braun Department of Electrical and Computer Engineering SyBBURE Searle Undergraduate Research Program Vanderbilt University Advanced Robotics and Control Laboratory Advanced Robotics and Control Laboratory Vanderbilt University

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This study introduces an innovative actuator that resembles a motor with a non-uniform permanent magnetic field. We have developed a prototype of the actuator by combining a standard motor, characterized by a uniform magnetic field, with a custom rotary magnetic spring exhibiting a non-uniform magnetic field. We have also presented a systematic computational approach to customize the magnetic field to minimize the energy consumption of the actuator when used for a user-defined oscillatory task. Experiments demonstrate that this optimized actuator significantly lowers energy consumption in a typical oscillatory task, such as pick-and-place or oscillatory limb motion during locomotion, compared to conventional motors. Our findings imply that incorporating task-optimized non-uniform permanent magnetic fields into conventional motors and direct-drive actuators could enhance the energy efficiency of robotic systems.

关键词： Actuators Energy consumption Limbs Systematics Prototypes Motors Minimization Magnetic fields Springs Standards

Safe Multi-Robotic Arm Interaction via 3D Convex Shapes

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arXiv 2025年

作者： Kaypak, Ali Umut Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical and Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Inter-robot collisions pose a significant safety risk when multiple robotic arms operate in close proximity. We present an online collision avoidance methodology leveraging 3D convex shape-based High-Order control Barrier Functions (HOCBFs) to address this issue. While prior works focused on using control Barrier Functions (CBFs) for human–robotic arm and single-arm collision avoidance, we explore the problem of collision avoidance between multiple robotic arms operating in a shared space. In our methodology, we utilize the proposed HOCBFs as centralized and decentralized safety filters. These safety filters are compatible with any nominal controller and ensure safety without significantly restricting the robots’ workspace. A key challenge in implementing these filters is the computational overhead caused by the large number of safety constraints and the computation of a Hessian matrix per constraint. We address this challenge by employing numerical differentiation methods to approximate computationally intensive terms. The effectiveness of our method is demonstrated through extensive simulation studies and real-world experiments with Franka research 3 robotic arms. © 2025, CC BY.

关键词： Robotic arms

Confidence-Aware Safe and Stable control of control-Affine Systems

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Confidence-Aware Safe and Stable Control of Control-Affine S...

American control Conference (ACC)

作者： Shiqing Wei Prashanth Krishnamurthy Farshad Khorrami Department of Electrical and Computer Engineering NYU Tandon Control/Robotics Research Laboratory School of Engineering 5 Metrotech Center Brooklyn NY USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer's estimation accuracy and the fulfillment of safety and control requirements.

关键词： Accuracy Observers control systems Eigenvalues and eigenfunctions Safety Trajectory Task analysis

Extension Principle and controller Design for Nonlinear Systems

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Extension Principle and Controller Design for Nonlinear Syst...

European control Conference (ECC)

作者： Altuğ İftar Farshad Khorrami Department of Electrical and Electronics Engineering Eskişehir Technical University Eskişehir Turkey Department of Electrical and Computer Engineering Control/Robotics Research Laboratory (CRRL) New York University 5 Metrotech Center Brooklyn NY U.S.A.

We generalize the principle of extension and controller design using this principle for nonlinear systems. We first define principles of inclusion and extension for nonlinear time-invariant (NLTI) systems. Thereafter, we present the necessary and sufficient conditions for a system to be an extension of another and show that when the expanded system is an extension of the original system, any controller designed for the expanded system can be contracted for implementation on the original system. Subsequently, we demonstrate how an overlappingly decomposed NLTI system can be expanded so that the expanded system is an extension of the original system, how decentralized controllers can be designed for the expanded system, and how these controllers can be contracted for implementation on the original system. An example is also provided to illustrate the extension and contraction concepts and show controller design using the proposed principles.

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Sailing Through Point Clouds: Safe Navigation Using Point Cloud Based control Barrier Functions

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arXiv 2024年

作者： Dai, Bolun Khorrambakht, Rooholla Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments. © 2024, CC BY.

关键词： Robot programming

Confidence-Aware Safe and Stable control of control-Affine Systems

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arXiv 2024年

作者： Wei, Shiqing Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Department of Electrical and Computer Engineering NYU Tandon School of Engineering 5 Metrotech Center BrooklynNY11201 United States

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer’s estimation accuracy and the fulfillment of safety and control requirements. © 2024, CC BY.

关键词： Lyapunov functions

Data-Efficient control Barrier Function Refinement

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Data-Efficient Control Barrier Function Refinement

American control Conference (ACC)

作者： Bolun Dai Heming Huang Prashanth Krishnamurthy Farshad Khorrami Electrical & Computer Engineering Department Control/Robotics Research Laboratory Tandon School of Engineering New York University Brooklyn NY

control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, a CBF provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a two-link arm.

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Data-Efficient control Barrier Function Refinement

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arXiv 2023年

作者： Dai, Bolun Huang, Heming Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

control barrier functions (CBFs) have been widely used for synthesizing controllers in safety-critical applications. When used as a safety filter, it provides a simple and computationally efficient way to obtain safe controls from a possibly unsafe performance controller. Despite its conceptual simplicity, constructing a valid CBF is well known to be challenging, especially for high-relative degree systems under nonconvex constraints. Recently, work has been done to learn a valid CBF from data based on a handcrafted CBF (HCBF). Even though the HCBF gives a good initialization point, it still requires a large amount of data to train the CBF network. In this work, we propose a new method to learn more efficiently from the collected data through a novel prioritized data sampling strategy. A priority score is computed from the loss value of each data point. Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF. Using our proposed approach, we can learn a valid CBF that recovers a larger portion of the true safe set using a smaller amount of data. The effectiveness of our method is demonstrated in simulation on a unicycle and a two-link arm. © 2023, CC BY.

关键词： controllers