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American Control Conference (ACC)

作者： Shiva Kumar Tekumatla Varun Gampa Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper presents an off-policy Gaussian Predictive Control (GPC) framework aimed at solving optimal control problems with a smaller computational footprint, thereby facilitating real-time applicability while ensuring critical safety considerations. The proposed controller imitates classical control methodologies by modeling the optimization process through a Gaussian process and employs Gaussian Process Regression to learn from the Model Predictive Control (MPC) algorithm. Notably, the Gaussian Process setup does not incorporate a built-in model, enhancing its applicability to a broad range of control problems. We applied this framework experimentally to a differential drive mobile robot, tasking it with trajectory tracking and obstacle avoidance. Leveraging the off-policy aspect, the controller demonstrated adaptability to diverse trajectories and obstacle behaviors. Simulation experiments confirmed the effectiveness of the proposed GPC method, emphasizing its ability to learn the dynamics of optimal control strategies. Consequently, our findings highlight the significant potential of off-policy Gaussian Predictive Control in achieving real-time optimal control for handling of robotic systems in safety-critical scenarios.

关键词： Adaptation models Trajectory tracking Computational modeling Optimal control Gaussian processes Predictive models Real-time systems

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Semantic Segmentation for Robotic Apple Harvesting: A Deep Learning Approach Leveraging U-Net, Synthetic Data, and Domain Adaptation

Semantic Segmentation for Robotic Apple Harvesting: A Deep L...

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International Conference on Ubiquitous Robots (UR)

作者： Ghokulji Selvaraj Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350361070

ISBN: (纸本)9798350361087

This paper introduces a deep learning-based semantic segmentation framework tailored for robotic apple harvesting, leveraging synthetic data generated within a 3D simulated apple orchard. The proposed simulation environment replicates real-world scenarios, encompassing challenges such as occlusion, variety in apple types, and changes in lighting conditions. This approach eliminates the extensive costs and complexities associated with collecting real-world datasets, particularly in unpredictable agricultural settings. The synthetic dataset, rendered from perspectives consistent with a robotic harvester's camera in the Gazebo physics engine, provides a comprehensive range of scenarios for robust model training. For validation, we deploy U-Net, a fully convolutional neural network, emphasizing its adaptability to domain shifts between synthetic and real-world data. By integrating strategies such as domain adaptation, data augmentation, and the inclusion of pre-trained ResNet-50 encoders in the U-Net framework, we demonstrate superior performance in detecting and segmenting apples in diverse real-world conditions compared to standard U-Net models and traditional computer vision techniques. The results highlight the potential of synthetic data in deep learning-based semantic segmentation, offering a cost-effective and scalable solution when real-world data is limited or hard to collect.

关键词： Training Adaptation models Three-dimensional displays Semantic segmentation Robot vision systems Lighting Standards

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PAAMP: Polytopic Action-Set And Motion Planning for Long Horizon Dynamic Motion Planning via Mixed Integer Linear programming

PAAMP: Polytopic Action-Set And Motion Planning for Long Hor...

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

作者： Akshay Jaitly Siavash Farzan Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Electrical Engineering Department California Polytechnic State University San Luis Obispo CA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Optimization methods for long-horizon, dynamically feasible motion planning in robotics tackle challenging nonconvex and discontinuous optimization problems. Traditional methods often falter due to the nonlinear characteristics of these problems. We introduce a technique that utilizes learned representations of the system, known as Polytopic Action Sets, to efficiently compute long-horizon trajectories. By employing a suitable sequence of Polytopic Action Sets, we transform the long-horizon dynamically feasible motion planning problem into a Linear program. This reformulation enables us to address motion planning as a Mixed Integer Linear program (MILP). We demonstrate the effectiveness of a Polytopic Action-Set and Motion Planning (PAAMP) approach by identifying swing-up motions for a torque-constrained pendulum as fast as 0.75 milliseconds. This approach is well-suited for solving complex motion planning and long-horizon Constraint Satisfaction Problems (CSPs) in dynamic and underactuated systems such as legged and aerial robots.

关键词： Dynamics Optimization methods Transforms Autonomous aerial vehicles Planning Trajectory Mixed integer linear programming Dynamic programming Intelligent robots

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Loss Distillation via Gradient Matching for Point Cloud Comp...

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

作者： Fangzhou Lin Haotian Liu Haoying Zhou Songlin Hou Kazunori D Yamada Gregory S. Fischer Yanhua Li Haichong K. Zhang Ziming Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Graduate School of Information Sciences Tohoku University Sendai Japan Dell Technologies Hopkinton MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Benchmark testing Iterative algorithms Tuning Optimization Intelligent robots Convergence

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Run-time task composition with safety semantics 24

Run-time task composition with safety semantics

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Proceedings of the 41st International Conference on Machine Learning

作者： Kevin Leahy Makai Mann Zachary Serlin Department of Robotics Engineering Worcester Polytechnic Institute Lexington MA MIT Lincoln Laboratory Lexington MA

Compositionality is a critical aspect of scalable system design. Here, we focus on Boolean composition of learned tasks as opposed to functional or sequential composition. Existing Boolean composition for Reinforcement Learning focuses on reaching a satisfying absorbing state in environments with discrete action spaces, but does not support composable safety (i.e., avoidance) constraints. We provide three contributions: i) introduce two distinct notions of compositional safety semantics; ii) show how to enforce either safety semantics, prove correctness, and analyze the trade-offs between the two safety notions; and iii) extend Boolean composition from discrete action spaces to continuous action spaces. We demonstrate these techniques using modified versions of value iteration in a grid world, Deep Q-Network (DQN) in a grid world with image observations, and Twin Delayed DDPG (TD3) in a continuous-observation and continuous-action Bullet physics environment.

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PFAS Incineration: Known Unknowns, Reaction Simulation, and ML to Predict Product Properties

PFAS Incineration: Known Unknowns, Reaction Simulation, and ...

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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Duncan Soiffer Tuomas Pyorre David Kenney Andrew Teixeira Department of Computer Science and Mathematical Sciences Worcester Polytechnic Institute Worcester MA United States Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA United States Department of Chemical Engineering Worcester Polytechnic Institute Worcester MA United States

ISBN: (数字)9798350309652

ISBN: (纸本)9798350309669

Per- and polyfluoroalkyl substances (PFAS) are a group of chemicals used for their impressive stability, a property which has also led them to be found polluting virtually every sector of the environment-including our own bodies. Presently, our ability to destroy PFAS is limited largely to incineration, whose products and side effects are poorly understood. Research on PFAS incineration is constrained by high experimental costs and difficulty in accurately measuring reaction products. This presents an opportunity for computer simulation and machine learning to provide valuable assistance. In this study, three kinds of thermal PFAS decomposition are simulated. Further, to broaden physical understanding of PFAS incineration products, a recent in-house machine learning model for predicting the octanol-water partition coefficient (LogP) of chemicals is improved and applied to simulation results. A decrease in LogP is observed in all treatments, with the most dramatic effect (-2 average LogP) observed in oxidative pyrolysis.

关键词： Costs Incineration Computer simulation Thermal decomposition Machine learning Predictive models Stability analysis

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SPADE: Spectroscopic Photoacoustic Denoising using an Analytical and Data-free Enhancement Framework

arXiv

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arXiv 2024年

作者： Lin, Fangzhou Gao, Shang Tang, Yichuan Ma, Xihan Murakami, Ryo Zhang, Ziming Obayemi, John D. Soboyejo, Winston O. Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States 60 Prescott Street WorcesterMA01605 United States Department of Mechanical & Materials Engineering Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States Department of Computer Science Worcester Polytechnic Institute 100 Institute Road WorcesterMA01609 United States

Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate and quantify chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, PA imaging is highly susceptible to noise, leading to a low signal-to-noise ratio (SNR) and compromised image quality. Furthermore, low SNR in spectral data adversely affects spectral unmixing outcomes, hindering accurate quantitative PA imaging. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. Moreover, spectral information preservation is unclear, limiting their adaptability for clinical usage. Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, in vivo, and ex vivo studies. These studies demonstrated that SPADE improved image SNR by over 15 dB in high noise cases and preserved spectral information (R > 0.8), outperforming conventional methods, especially in low SNR conditions. SPADE presents a promising solution for preserving the accuracy of quantitative PA imaging in clinical applications where noise reduction and spectral preservation are critical. © 2024, CC BY-NC-ND.

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ES-HPC-MPC: Exponentially Stable Hybrid Perception Constrained MPC for Quadrotor with Suspended Payloads

arXiv

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arXiv 2025年

作者： Recalde, Luis F. Sarvaiya, Mrunal Loianno, Giuseppe Li, Guanrui Worcester Polytechnic Institute Robotics Engineering WorcesterMA01609 United States New York University New YorkNY11201 United States

Aerial transportation using quadrotors with cable-suspended payloads holds great potential for applications in disaster response, logistics, and infrastructure maintenance. However, their hybrid and underactuated dynamics pose significant control and perception challenges. Traditional approaches often assume a taut cable condition, limiting their effectiveness in real-world applications where slack-to-taut transitions occur due to disturbances. We introduce ES-HPC-MPC, a model predictive control framework that enforces exponential stability and perception-constrained control under hybrid dynamics. Our method leverages Exponentially Stabilizing Control Lyapunov Functions (ES-CLFs) to enforce stability during the tasks and Control Barrier Functions (CBFs) to maintain the payload within the onboard camera’s field of view (FoV). We validate our method through both simulation and real-world experiments, demonstrating stable trajectory tracking and reliable payload perception. We validate that our method maintains stability and satisfies perception constraints while tracking dynamically infeasible trajectories and when the system is subjected to hybrid mode transitions caused by unexpected disturbances. © 2025, CC BY-NC-ND.

关键词： Lyapunov functions

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A Passive Model-Based Error Compensation Approach for Bevel Tip Needle Deflection

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Journal of Medical robotics Research 2023年第3-4期8卷

作者： Tavakkolmoghaddam, Farid Fischer, Gregory S. Robotics Engineering Department Worcester Polytechnic Institute Worcester MA United States

The interaction between an asymmetric (bevel) tipped needle and the surrounding tissue in image-guided percutaneous interventions, such as in prostate needle biopsy, can result in the deflection of the needle tip and cause significant targeting error. Researchers have extensively investigated different aspects of the needle-tissue interaction and proposed various solutions to mitigate this issue. While there are several promising approaches, most require complex software or hardware which makes them difficult to be deployed for clinical use. In this paper, we present a predictive model-based approach to passively compensate for undesired deflection of the needle tip prior to the initial insertion into the tissue. In this approach, an approximation of the initial deflection angle of the needle tip and the natural curvature of the needle path are utilized to simulate the insertion and predict the tip deflection prior to insertion. The model then calculates a modified needle entry point which accounts for the predicted tip deflection during the insertion. To create the model, we first collected a set of needle insertion data utilizing nonhomogeneous gelatin phantoms and ex vivo bovine tissue, using an electromagnetic (EM) tracking system and a needle with an EM sensor embedded at its tip. The collected data were then used to find the model parameters, namely, the initial deflection angle and the needle path curvature. After creating the model, a validation study consisting of two sets of insertions, one with and another without compensation, was carried out to evaluate the performance of our proposed model in compensating for the tip deflection error. The results demonstrate an average of 77% targeting accuracy improvement for insertions with modified entry points based on our model's prediction compared with the uncompensated insertions. © 2023 World Scientific Publishing Company.

关键词： image-guided therapy needle deflection compensation Robot-assisted interventions

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Realistic Data Generation for 6D Pose Estimation of Surgical Instruments

Realistic Data Generation for 6D Pose Estimation of Surgical...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Juan Antonio Barragan Jintan Zhang Haoying Zhou Adnan Munawar Peter Kazanzides Department of Computer Science Johns Hopkins University Baltimore MD USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Automation in surgical robotics has the potential to improve patient safety and surgical efficiency, but it is difficult to achieve due to the need for robust perception algorithms. In particular, 6D pose estimation of surgical instruments is critical to enable the automatic execution of surgical maneuvers based on visual feedback. In recent years, supervised deep learning algorithms have shown increasingly better performance at 6D pose estimation tasks; yet, their success depends on the availability of large amounts of annotated data. In household and industrial settings, synthetic data, generated with 3D computer graphics software, has been shown as an alternative to minimize annotation costs of 6D pose datasets. However, this strategy does not translate well to surgical domains as commercial graphics software have limited tools to generate images depicting realistic instrument-tissue interactions. To address these limitations, we propose an improved simulation environment for surgical robotics that enables the automatic generation of large and diverse datasets for 6D pose estimation of surgical instruments. Among the improvements, we developed an automated data generation pipeline and an improved surgical scene. To show the applicability of our system, we generated a dataset of 7.5k images with pose annotations of a surgical needle that was used to evaluate a state-of-the-art pose estimation network. The trained model obtained a mean translational error of 2.59mm on a challenging dataset that presented varying levels of occlusion. These results highlight our pipeline’s success in training and evaluating novel vision algorithms for surgical robotics applications.

关键词： Training Visualization Medical robotics Three-dimensional displays Annotations Instruments Pose estimation

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