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IEEE International Conference on robotics and Automation (ICRA)

作者： Albert Enyedy Ashay Aswale Berk Calli Michael Gennert Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents an image-based visual servoing scheme that can control robotic manipulators in 3D space using 2D stereo images without needing to perform stereo reconstruction. We use a stereo camera in an eye-to-hand configuration for controlling the robot to reach target positions by directly mapping image space errors to joint space actuation. We achieve convergence without a-priori knowledge of the target object, a reference 2D image, or 3D data. By doing so, we can reach targets in unstructured environments using high-resolution RGB images instead of utilizing relatively noisy depth data. We conduct several experiments on two different physical robots. The Panda 7DOF arm grasps a static target in 3D space, grasps a pitcher handle, and picks and places a box by determining the approach angle using 2D image features, demonstrating that this algorithm can be used for grasping practical objects in 3D space using only 2D image features for feedback. Our second platform, the Atlas humanoid robot, reaches a target from an unknown starting configuration, demonstrating that this controller achieves convergence to a target, even with the uncertainties introduced by walking to a new location. We believe that this algorithm is a step towards enabling intuitive interfaces that allow a user to initiate a grasp on an object by specifying a grasping point in a 2D image.

关键词： Legged locomotion Three-dimensional displays Uncertainty Grasping Aerospace electronics Manipulators Visual servoing

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Human-Robot Complementary Collaboration for Flexible and Precision Assembly

Human-Robot Complementary Collaboration for Flexible and Pre...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Shichen Cao Jing Xiao Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper addresses human-robot collaborative (HRC) precision assembly that complements natural human ability and the strength of an autonomous robot system. Our approach enables both flexibility and efficiency of tight-clearance assembly of various complex-shaped parts in the presence of uncertainty without requiring assembly skills and knowledge of robotics from the human operator. We demonstrated the effectiveness of our approach in a variety of experiments and comparisons with other HRC assembly approaches.

关键词： Training Robotic assembly Productivity Human computer interaction Uncertainty Collaboration Manipulators

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Thermal Ablation Therapy Control with Tissue Necrosis-driven Temperature Feedback Enabled by Neural State Space Model with Extended Kalman Filter

Thermal Ablation Therapy Control with Tissue Necrosis-driven...

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

作者： Ryo Murakami Satoshi Mori Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA The United States Department of Biomedical Engineering Robotics Engineering and Computer Science Engineering Worcester Polytechnic Institute Worcester MA The United States

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Thermal ablation therapy is a major minimally invasive treatment. One of the challenges is that the targeted region and therapeutic progression are often invisible to clinicians, requiring feedback provided in numerical information or imaging. Several emerging imaging modalities offer visualization of the ablation-induced necrosis formation; however, relying solely on necrosis monitoring can result in tissue overheating and endangering patients. Some of the necrosis monitoring modalities are known for their capabilities in temperature sensing, but the principles on which they are based have several limitations, such as sensitivity to the tissue motion and their environment. In this study, we propose a necrosis progression-based temperature estimation technique as an added safety feature for avoiding overheating. This model-based method does not require additional sensing hardware. It is designed to work as an independent estimator or a complimentary estimation component with other thermometers for improved robustness. For this objective, the Neural State Space model is used to approximate the ablation therapy, whose theoretical models involve nonlinear partial differential equations. Then, the Extended Kalman Filter is designed based on the model. The simulation study shows the estimation module robustly estimates the tissue temperature under several types of noise. The maximum estimation error observed before terminating ablation was around 1 °C, and the desired safety feature was successfully demonstrated. The estimator is expected to be used in a variety of necrosis monitoring modalities to guarantee more precise and safer treatment. More ambitiously, the architecture with the Neural State Space model and Extended Kalman Filter is generalizable to other medical/biological procedures involving nonlinear and patient/environment-specific physics and even to procedures having no reliable theoretical models.

关键词： Thermometers Estimation Medical treatment Mathematical models Robustness Sensors Safety Temperature control Kalman filters Monitoring

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Topological Guided Actor-Critic Modular Learning of Continuous Systems with Temporal Objectives

arXiv

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arXiv 2023年

作者： Li, Lening Qian, Zhentian Robotics Engineering Program Worcester Polytechnic Institute WorcesterMA01609 United States

This work investigates the formal policy synthesis of continuous-state stochastic dynamic systems given high-level specifications in linear temporal logic. To learn an optimal policy that maximizes the satisfaction probability, we take a product between a dynamic system and the translated automaton to construct a product system on which we solve an optimal planning problem. Since this product system has a hybrid product state space that results in reward sparsity, we introduce a generalized optimal backup order, in reverse to the topological order, to guide the value backups and accelerate the learning process. We provide the optimality proof for using the generalized optimal backup order in this optimal planning problem. Further, this paper presents an actor-critic reinforcement learning algorithm when topological order applies. This algorithm leverages advanced mathematical techniques and enjoys the property of hyperparameter self-tuning. We provide proof of the optimality and convergence of our proposed reinforcement learning algorithm. We use neural networks to approximate the value function and policy function for hybrid product state space. Furthermore, we observe that assigning integer numbers to automaton states can rank the value or policy function approximated by neural networks. To break the ordinal relationship, we use an individual neural network for each automaton state’s value (policy) function, termed modular learning. We conduct two experiments. First, to show the efficacy of our reinforcement learning algorithm, we compare it with baselines on a classic control task, CartPole. Second, we demonstrate the empirical performance of our formal policy synthesis framework on motion planning of a Dubins car with a temporal specification. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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Image-Based Roadmaps for Vision-Only Planning and Control of Robotic Manipulators

arXiv

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arXiv 2025年

作者： Chatterjee, Sreejani Gandhi, Abhinav Calli, Berk Chamzas, Constantinos Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

This work presents a motion planning framework for robotic manipulators that computes collision-free paths directly in image space. The generated paths can then be tracked using vision-based control, eliminating the need for an explicit robot model or proprioceptive sensing. At the core of our approach is the construction of a roadmap entirely in image space. To achieve this, we explicitly define sampling, nearest-neighbor selection, and collision checking based on visual features rather than geometric models. We first collect a set of image-space samples by moving the robot within its workspace, capturing keypoints along its body at different configurations. These samples serve as nodes in the roadmap, which we construct using either learned or predefined distance metrics. At runtime, the roadmap generates collision-free paths directly in image space, removing the need for a robot model or joint encoders. We validate our approach through an experimental study in which a robotic arm follows planned paths using an adaptive vision-based control scheme to avoid obstacles. The results show that paths generated with the learned-distance roadmap achieved 100% success in control convergence, whereas the predefined image-space distance roadmap enabled faster transient responses but had a lower success rate in convergence. © 2025, CC BY.

关键词： Transient analysis

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Liver Necrotic Mapping Using Diffusing Illumination Photoacoustic Imaging

Liver Necrotic Mapping Using Diffusing Illumination Photoaco...

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Photons Plus Ultrasound: Imaging and Sensing 2025

作者： Gao, Shang Hua, Yanbo Bhagavatula, Sharath K. Liu, Guigen Jonas, Oliver Zhang, Haichong K. Department of Robotics Engineering Worcester Polytechnic Institute MA United States Department of Biomedical Engineering Worcester Polytechnic Institute MA United States Department of Computer Science Worcester Polytechnic Institute MA United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School MA United States

ISBN: (纸本)9781510683860

Liver cancer treatments often face challenges, particularly when tumors are considered surgically unresectable. Percutaneous radiofrequency ablation (RFA) offers a minimally invasive alternative but can have higher recurrence rates due to incomplete ablations. Standard non-contrast ultrasound imaging cannot distinguish ablation-induced necrosis, limiting ability to monitor treatment and ensure complete tumor coverage during the ablation procedure. Photoacoustic (PA) imaging, which uses light to generate ultrasound signals, has shown promise in distinguishing between ablated and normal tissues through spectroscopic PA (sPA) imaging. However, conventional light delivery methods are unsuitable for minimally invasive procedures due to bulky fiber bundles. Optical fibers, though suitable for less invasive insertion, have limited illumination volume because of the confined light delivery only at the fiber tip. This study introduces a diffusing fiber approach for effective light delivery within the liver, enhancing the PA excitation area compared to conventional tip illumination. The diffusing fiber was tested in both phantom and ex vivo liver tissue studies. Results demonstrated that the diffusing fiber provided a wider PA excitation area and more homogeneous illumination. In an ex vivo porcine liver, the diffusing fiber accurately mapped the ablated region with minimal deviation from gross pathological measurements. Our findings suggest that this diffusing fiber approach could significantly improve the accuracy of PA imaging for liver ablation, potentially reducing recurrence rates by ensuring complete tumor ablation and minimizing complications by enabling real time monitoring. This innovation paves the way for more reliable and effective minimally invasive treatments for liver cancer. © 2025 SPIE.

关键词： Photoacoustic microscopy

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Occlusion-Based Cooperative Transport for Concave Objects with a Swarm of Miniature Mobile Robots

arXiv

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arXiv 2024年

作者： Nirgude, Sanjuksha Nema, Animesh Jagetia, Aishwary Robotics Engineering Department Worcester Polytechnic Institute WorcesterMA United States

An occlusion based strategy for collective transport of a concave object using a swarm of mobile robots has been proposed in this paper. We aim to overcome the challenges of transporting concave objects using decentralized approach. The interesting aspect of this task is that the agents have no prior knowledge about the geometry of the object and do not explicitly communicate with each other. The concept is to eliminate the concavity of the object by filling a number of robots in its cavity and then carry out an occlusion based transport strategy on the newly formed convex object or"pseudo object". We divide our work into two parts- concavity filling of various concave objects and occlusion based collective transport of convex objects. © 2024, CC0.

关键词： Mobile robots

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Expansion-GRR: Efficient Generation of Smooth Global Redundancy Resolution Roadmaps

Expansion-GRR: Efficient Generation of Smooth Global Redunda...

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

作者： Zhuoyun Zhong Zhi Li Constantinos Chamzas Department of Robotics Engineering Worcester Polytechnic Institute (WPI) Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Global redundancy resolution (GRR) roadmap is a novel concept in robotics that facilitates the mapping from task space paths to configuration space paths in a legible, predictable, and repeatable way. Such roadmaps could find widespread utility in applications such as safe teleoperation, consistent path planning, and motion primitives generation. However, previous methods to compute GRR roadmaps often necessitate a lengthy computation time and produce non-smooth paths, limiting their practical efficacy. To address this challenge, we introduce a novel method EXPANSION-GRR that leverages efficient configuration space projections and enables a rapid generation of smooth roadmaps that satisfy the task constraints. Additionally, we propose a simple multi-seed strategy that further enhances the final quality. We conducted experiments in simulation with a 5-link planar manipulator and a Kinova arm. We were able to generate the GRR roadmaps up to 2 orders of magnitude faster while achieving higher smoothness. We also demonstrate the utility of the GRR roadmaps in teleoperation tasks where our method outperformed prior methods and reactive IK solvers in terms of success rate and solution quality.

关键词： Limiting Redundancy Manipulators Path planning Intelligent robots

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CNN-Based Task State Estimation for Safer Automation of Oxy-Fuel Metal Cutting

CNN-Based Task State Estimation for Safer Automation of Oxy-...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： James Akl Shreedhar Kodate Berk Calli Robotics Engineering Department Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA

The industrial operation of oxy-fuel metal cutting via gas torches involves tasks such as ignition, preheating, and combustion along the target surface. Automated oxy-fuel cutting systems are exposed to risks and anomalies that can lead to incorrect actions and safety hazards. In this paper, we develop a classifier for online task state estimation to assess the cutting robot's actions, detect anomalies, and reduce the risk of hazards. Using representative footage from our robotic cutting experiments, we curate an image dataset labeled with four types of cutting task states. Using deep learning methods, we design and train a convolutional neural network model for classifying the cutting task state from input images. The classifier architecture is optimized for rapid inferences during online estimation. After evaluation, our classifier achieves an overall accuracy of 93.8 % with high inference speeds on two types of representative hardware. Our ‘Oxy-fuel Cutting Task State’ (OCTS) dataset is available at ***/10.5281/zenodo.7734951.

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Multilateral Multimodal Human-Robot Collaboration for Robotic Nursing Assistance: Prototype System and Preliminary User Study

Multilateral Multimodal Human-Robot Collaboration for Roboti...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Lorena Maria Genua Nikita Boguslavskii Zhi Li Robotics Engineering Department Worcester Polytechnic Institute (WPI) Worcester MA USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

In this paper, we introduce an innovative robotic nursing assistance system with multilateral multimodal human-robot (MMHR) collaboration, enabling nursing robots to be assisted by remote and on-site operators. Through our augmented reality (AR) interfaces, the remote and local operators can monitor the robots’ operations, issue task and action commands, and facilitate collaborative assistance and information exchange via AR cues or verbal communication. Our preliminary user study evaluated the usability of the prototype system and validated the efficacy of our MMHR collaboration in a representative nursing assistance task scenario. The results show significant improvements in overall task efficiency for the remote operator and reveal human strategies and rationales in the spontaneous multilateral human-robot collaboration.

关键词： Collaboration Prototypes Human-robot interaction Medical services Usability Robots Monitoring Information exchange Augmented reality

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