Tensegrity Robot Endcap-Ground Contact Estimation with Symmetry-aware Heterogeneous Graph Neural Network

• URL: http://arxiv.org/abs/2603.02596v1
• Date: Tue, 03 Mar 2026 04:45:43 GMT
• Title: Tensegrity Robot Endcap-Ground Contact Estimation with Symmetry-aware Heterogeneous Graph Neural Network
• Abstract summary: This paper introduces a symmetry-aware heterogeneous graph neural network (Sym-HGNN) that infers contact states directly from proprioceptive measurements.<n>The network incorporates the robot's dihedral symmetry $D_3$ into the message-passing process to enhance sample efficiency and generalization.
• Score: 16.823879279044252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tensegrity robots possess lightweight and resilient structures but present significant challenges for state estimation due to compliant and distributed ground contacts. This paper introduces a symmetry-aware heterogeneous graph neural network (Sym-HGNN) that infers contact states directly from proprioceptive measurements, including IMU and cable-length histories, without dedicated contact sensors. The network incorporates the robot's dihedral symmetry $D_3$ into the message-passing process to enhance sample efficiency and generalization. The predicted contacts are integrated into a state-of-the-art contact-aided invariant extended Kalman filter (InEKF) for improved pose estimation. Simulation results demonstrate that the proposed method achieves up to 15% higher accuracy and 5% higher F1-score using only 20% of the training data compared to the CNN and MI-HGNN baselines, while maintaining low-drift and physically consistent state estimation results comparable to ground truth contacts. This work highlights the potential of fully proprioceptive sensing for accurate and robust state estimation in tensegrity robots. Code available at: https://github.com/Jonathan-Twz/Tensegrity-Sym-HGNN

Related papers

• Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets [45.88028371034407]
  The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics.<n>This study evaluated the viability of using energy-derived features as either a standalone classification modality or as supplementary input to inertial data.
  arXiv  Detail & Related papers  (2026-06-17T05:24:32Z)
• Design and Validation of a Lightweight 1D CNN for Affective Touch Classification in Soft Plush Companions [59.49967357689445]
  Soft, sensorized companions offer a physically safe and emotionally intuitive interface for socially assistive technologies.<n>This study presents a complete open-source dilated real-based framework for the development and validation of deep learning models for affective touch recognition in soft interactive companions.
  arXiv  Detail & Related papers  (2026-04-16T10:20:05Z)
• Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems [0.0]
  Real-time particle transverse momentum ($p_T$) estimation in high-energy physics demands efficient algorithms under strict hardware constraints.<n>We propose a physics-informed graph neural network (GNN) framework that systematically encodes detector geometry and physical observables.<n>Our co-design methodology yields superior accuracy-efficiency trade-offs compared to existing baselines.
  arXiv  Detail & Related papers  (2025-07-25T12:19:57Z)
• Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout [62.73150122809138]
  Federated Learning (FL) is a promising distributed machine learning approach that enables collaborative training of a global model using multiple edge devices.<n>We propose the FedDHAD FL framework, which comes with two novel methods: Dynamic Heterogeneous model aggregation (FedDH) and Adaptive Dropout (FedAD)<n>The combination of these two methods makes FedDHAD significantly outperform state-of-the-art solutions in terms of accuracy (up to 6.7% higher), efficiency (up to 2.02 times faster), and cost (up to 15.0% smaller)
  arXiv  Detail & Related papers  (2025-07-14T16:19:00Z)
• MIXPINN: Mixed-Material Simulations by Physics-Informed Neural Network [1.275845610262865]
  Traditional Finite Element Method (FEM)-based simulations are computationally expensive and impractical for real-time scenarios.<n>We introduce MIXPINN, a physics-informed Graph Neural Network (GNN) framework for mixed-material simulations.<n>By leveraging a graph-based representation of biomechanical structures, MIXPINN learns high-fidelity deformations from FEM-generated data and achieves real-time inference with sub-millimeter accuracy.
  arXiv  Detail & Related papers  (2025-03-17T12:48:29Z)
• A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition [0.2356141385409842]
  This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition.<n>The proposed model boasts an accuracy of 99.7% in classifying 53 classes in just 25 milliseconds.
  arXiv  Detail & Related papers  (2025-03-12T04:01:32Z)
• emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography [47.160223334501126]
  emg2qwerty is a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard.<n>With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date.<n>We show strong baseline performance on predicting key-presses using sEMG signals alone.
  arXiv  Detail & Related papers  (2024-10-26T05:18:48Z)
• A Versatile and Differentiable Hand-Object Interaction Representation [2.184775414778289]
  Coarse Hand-Object Interaction Representation (CHOIR) is a novel, versatile and fully differentiable field for HOI modelling.<n>CHOIR leverages discrete unsigned distances for continuous shape and pose encoding.<n>JointDiffusion is a diffusion model to learn a grasp distribution conditioned on noisy hand-object interactions.
  arXiv  Detail & Related papers  (2024-09-25T12:06:30Z)
• Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach [0.3222802562733786]
  We present a pioneering deep learning architecture tailored for the binary classification of subjects. Our model harnesses the power of 1D-Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Graph Neural Network (GNN) layers. Our proposed model achieves exceptional performance metrics, boasting accuracy, precision, recall, and F1 score values of 99.51%, 99.57%, 99.71%, and 99.64%, respectively.
  arXiv  Detail & Related papers  (2024-04-09T15:19:13Z)
• Analyzing Participants' Engagement during Online Meetings Using Unsupervised Remote Photoplethysmography with Behavioral Features [50.82725748981231]
  Engagement measurement finds application in healthcare, education, services. Use of physiological and behavioral features is viable, but impracticality of traditional physiological measurement arises due to the need for contact sensors. We demonstrate the feasibility of the unsupervised photoplethysmography (rmography) as an alternative for contact sensors.
  arXiv  Detail & Related papers  (2024-04-05T20:39:16Z)
• Collaborative Learning with a Drone Orchestrator [79.75113006257872]
  A swarm of intelligent wireless devices train a shared neural network model with the help of a drone. The proposed framework achieves a significant speedup in training, leading to an average 24% and 87% saving in the drone hovering time.
  arXiv  Detail & Related papers  (2023-03-03T23:46:25Z)
• Neural network enhanced measurement efficiency for molecular groundstates [63.36515347329037]
  We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians. We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
  arXiv  Detail & Related papers  (2022-06-30T17:45:05Z)

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.