A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

• URL: http://arxiv.org/abs/2501.14678v1
• Date: Fri, 24 Jan 2025 17:57:00 GMT
• Title: A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model
• Authors: Muhammad Hanif Lashari, Shakil Ahmed, Wafa Batayneh, Ashfaq Khokhar,
• Abstract summary: This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation.<n>A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework's superior performance in handling position prediction.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise and real-time estimation of the robotic arm's position on the patient's side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation. Additionally, it combines a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of O(L log L). The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90 percent under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework's superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery.

Related papers

• Data-Driven Dynamic Parameter Learning of manipulator robots [0.8679862302950613]
  We propose a Transformer-based approach for dynamic parameter estimation.<n>The dataset consists of 8,192 robots with varied inertial and frictional properties.<n>Our model effectively captures both temporal and spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-09T16:15:58Z)
• TIGeR: Tool-Integrated Geometric Reasoning in Vision-Language Models for Robotics [53.442362491589726]
  We present TIGeR (Tool-Integrated Geometric Reasoning), a novel framework that transforms Vision-Language Models (VLMs) into geometric computers.<n>Rather than attempting to internalize complex geometric operations within neural networks, TIGeR empowers models to recognize geometric reasoning requirements.<n>We show that TIGeR achieves SOTA performance on geometric reasoning benchmarks while demonstrating centimeter-level precision in real-world robotic manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-08T16:20:23Z)
• Advanced Hybrid Transformer LSTM Technique with Attention and TS Mixer for Drilling Rate of Penetration Prediction [1.2432046687586285]
  Rate of Penetration (ROP) is crucial for optimizing drilling operations.<n>Traditional empirical, physics-based, and basic machine learning models often fail to capture intricate temporal and contextual relationships.<n>We propose a novel hybrid deep learning architecture integrating Long Short-Term Memory (LSTM) networks, Transformer encoders, Time-Series Mixer (TS-Mixer) blocks.
  arXiv  Detail & Related papers  (2025-08-07T09:45:56Z)
• RoHOI: Robustness Benchmark for Human-Object Interaction Detection [84.78366452133514]
  Human-Object Interaction (HOI) detection is crucial for robot-human assistance, enabling context-aware support.<n>We introduce the first benchmark for HOI detection, evaluating model resilience under diverse challenges.<n>Our benchmark, RoHOI, includes 20 corruption types based on the HICO-DET and V-COCO datasets and a new robustness-focused metric.
  arXiv  Detail & Related papers  (2025-07-12T01:58:04Z)
• VAE-based Feature Disentanglement for Data Augmentation and Compression in Generalized GNSS Interference Classification [42.14439854721613]
  We propose variational autoencoders (VAEs) for disentanglement to extract essential latent features that enable accurate classification of interferences. Our proposed VAE achieves a data compression rate ranging from 512 to 8,192 and achieves an accuracy up to 99.92%.
  arXiv  Detail & Related papers  (2025-04-14T13:38:00Z)
• EfficientPose 6D: Scalable and Efficient 6D Object Pose Estimation [4.595205112368888]
  This study focuses on developing a fast and scalable set of pose estimators based on GDRNPP to meet or exceed current benchmarks in accuracy and robustness. We propose the AMIS algorithm to tailor the utilized model according to an application-specific trade-off between inference time and accuracy.
  arXiv  Detail & Related papers  (2025-02-19T19:21:23Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  We introduce a novel framework for learning world models.<n>By providing a scalable and robust framework, we pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• EVA-S2PLoR: Decentralized Secure 2-party Logistic Regression with A Subtly Hadamard Product Protocol (Full Version) [2.1010315462623184]
  This paper proposes an efficient, verifiable, and accurate security 2-party logistic regression framework (EVA-S2PLoR)<n>It achieves accurate nonlinear function through a subtly secure hadamard product protocol and its derived protocols.<n>High efficiency and precision are guaranteed by the asynchronous flow on floating point numbers and the few number of fixed communication rounds.
  arXiv  Detail & Related papers  (2025-01-09T13:19:59Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• PIVOT-R: Primitive-Driven Waypoint-Aware World Model for Robotic Manipulation [68.17081518640934]
  We propose a PrIrmitive-driVen waypOinT-aware world model for Robotic manipulation (PIVOT-R) PIVOT-R consists of a Waypoint-aware World Model (WAWM) and a lightweight action prediction module. Our PIVOT-R outperforms state-of-the-art open-source models on the SeaWave benchmark, achieving an average relative improvement of 19.45% across four levels of instruction tasks.
  arXiv  Detail & Related papers  (2024-10-14T11:30:18Z)
• RmGPT: Rotating Machinery Generative Pretrained Model [20.52039868199533]
  We propose RmGPT, a unified model for diagnosis and prognosis tasks. RmGPT introduces a novel token-based framework, incorporating Signal Tokens, Prompt Tokens, Time-Frequency Task Tokens and Fault Tokens. In experiments, RmGPT significantly outperforms state-of-the-art algorithms, achieving near-perfect accuracy in diagnosis tasks and exceptionally low errors in prognosis tasks.
  arXiv  Detail & Related papers  (2024-09-26T07:40:47Z)
• Enhancing Precision in Tactile Internet-Enabled Remote Robotic Surgery: Kalman Filter Approach [0.0]
  This paper presents a Kalman Filter (KF) based computationally efficient position estimation method. The study also assume no prior knowledge of the dynamic system model of the robotic arm system. We investigate the effectiveness of KF to determine the position of the Patient Side Manipulator (PSM) under simulated network conditions.
  arXiv  Detail & Related papers  (2024-06-06T20:56:53Z)
• OptiState: State Estimation of Legged Robots using Gated Networks with Transformer-based Vision and Kalman Filtering [42.817893456964]
  State estimation for legged robots is challenging due to their highly dynamic motion and limitations imposed by sensor accuracy. We propose a hybrid solution that combines proprioception and exteroceptive information for estimating the state of the robot's trunk. This framework not only furnishes accurate robot state estimates, but can minimize the nonlinear errors that arise from sensor measurements and model simplifications through learning.
  arXiv  Detail & Related papers  (2024-01-30T03:34:25Z)
• Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression [1.4528189330418977]
  Uncertainty estimation in machine learning is paramount for enhancing the reliability and interpretability of predictive models. We present an adaptation of the Multiple-Input Multiple-Output (MIMO) framework for pixel-wise regression tasks.
  arXiv  Detail & Related papers  (2023-08-14T22:08:28Z)
• Consensus-Adaptive RANSAC [104.87576373187426]
  We propose a new RANSAC framework that learns to explore the parameter space by considering the residuals seen so far via a novel attention layer. The attention mechanism operates on a batch of point-to-model residuals, and updates a per-point estimation state to take into account the consensus found through a lightweight one-step transformer.
  arXiv  Detail & Related papers  (2023-07-26T08:25:46Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Domain Adaptive Robotic Gesture Recognition with Unsupervised Kinematic-Visual Data Alignment [60.31418655784291]
  We propose a novel unsupervised domain adaptation framework which can simultaneously transfer multi-modality knowledge, i.e., both kinematic and visual data, from simulator to real robot. It remedies the domain gap with enhanced transferable features by using temporal cues in videos, and inherent correlations in multi-modal towards recognizing gesture. Results show that our approach recovers the performance with great improvement gains, up to 12.91% in ACC and 20.16% in F1score without using any annotations in real robot.
  arXiv  Detail & Related papers  (2021-03-06T09:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.