Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs

• URL: http://arxiv.org/abs/2202.00332v1
• Date: Tue, 1 Feb 2022 11:01:09 GMT
• Title: Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
• Authors: Timon Felske, Stefan L\"udtke, Sebastian Bader, Thomas Kirste
• Abstract summary: We study sensor-based human activity recognition in manual work processes like assembly tasks. In our approach, system states are represented by multi-hypergraphs, and the system dynamics is modeled by graph rewriting rules. We show a preliminary concept that allows to represent distributions over multi-hypergraphs more compactly than by full enumeration, and present an inference algorithm that works directly on this compact representation.
• Score: 1.2961180148172198
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from sensor observations by recursive Bayesian filtering can be very challenging, due to the combinatorial explosion in the number of system states. To alleviate this problem, we propose an efficient Bayesian filtering model for such processes. In our approach, system states are represented by multi-hypergraphs, and the system dynamics is modeled by graph rewriting rules. We show a preliminary concept that allows to represent distributions over multi-hypergraphs more compactly than by full enumeration, and present an inference algorithm that works directly on this compact representation. We demonstrate the applicability of the algorithm on a real dataset.

Related papers

• Bisimulation Learning [55.859538562698496]
  We compute finite bisimulations of state transition systems with large, possibly infinite state space. Our technique yields faster verification results than alternative state-of-the-art tools in practice.
  arXiv  Detail & Related papers  (2024-05-24T17:11:27Z)
• Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP [81.00800920928621]
  We study representation learning in partially observable Markov Decision Processes (POMDPs) We first present an algorithm for decodable POMDPs that combines maximum likelihood estimation (MLE) and optimism in the face of uncertainty (OFU) We then show how to adapt this algorithm to also work in the broader class of $gamma$-observable POMDPs.
  arXiv  Detail & Related papers  (2023-06-21T16:04:03Z)
• Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems [38.23826389188657]
  We present a deep state-space model which employs graph neural networks in order to model the underlying interacting dynamical system. The predictive distribution is multimodal and has the form of a Gaussian mixture model, where the moments of the Gaussian components can be computed via deterministic moment matching rules. Our moment matching scheme can be exploited for sample-free inference, leading to more efficient and stable training compared to Monte Carlo alternatives.
  arXiv  Detail & Related papers  (2023-05-02T20:30:23Z)
• Dealing with Collinearity in Large-Scale Linear System Identification Using Gaussian Regression [3.04585143845864]
  We consider estimation of networks consisting of several interconnected dynamic systems. We develop a strategy cast in a Bayesian regularization framework where any impulse response is seen as realization of a zero-mean Gaussian process. We design a novel Markov chain Monte Carlo scheme able to reconstruct the impulse responses posterior by efficiently dealing with collinearity.
  arXiv  Detail & Related papers  (2023-02-21T19:35:47Z)
• Subgraph Frequency Distribution Estimation using Graph Neural Networks [17.02487540304784]
  We propose GNNS, a novel representational learning framework that utilizes graph neural networks to sample subgraphs efficiently for estimating their frequency distribution. Our framework includes an inference model and a generative model that learns hierarchical embeddings of nodes, subgraphs, and graph types. With the learned model and embeddings, subgraphs are sampled in a highly scalable and parallel way and the frequency distribution estimation is then performed based on these sampled subgraphs.
  arXiv  Detail & Related papers  (2022-07-14T06:23:38Z)
• Assembly Planning from Observations under Physical Constraints [65.83676649042623]
  The proposed algorithm uses a simple combination of physical stability constraints, convex optimization and Monte Carlo tree search to plan assemblies. It is efficient and, most importantly, robust to the errors in object detection and pose estimation unavoidable in any real robotic system.
  arXiv  Detail & Related papers  (2022-04-20T16:51:07Z)
• Principled inference of hyperedges and overlapping communities in hypergraphs [0.0]
  We propose a framework based on statistical inference to characterize the structural organization of hypergraphs. We show strong performance in hyperedge prediction tasks, detecting communities well aligned with the information carried by interactions, and robustness against addition of noisy hyperedges.
  arXiv  Detail & Related papers  (2022-04-12T09:13:46Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Imitation of Manipulation Skills Using Multiple Geometries [20.21868546298435]
  We propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to represent an observed movement. We apply our approach to grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot.
  arXiv  Detail & Related papers  (2022-03-02T15:19:33Z)
• Deep Shells: Unsupervised Shape Correspondence with Optimal Transport [52.646396621449]
  We propose a novel unsupervised learning approach to 3D shape correspondence. We show that the proposed method significantly improves over the state-of-the-art on multiple datasets.
  arXiv  Detail & Related papers  (2020-10-28T22:24:07Z)
• Image Matching across Wide Baselines: From Paper to Practice [80.9424750998559]
  We introduce a comprehensive benchmark for local features and robust estimation algorithms. Our pipeline's modular structure allows easy integration, configuration, and combination of different methods. We show that with proper settings, classical solutions may still outperform the perceived state of the art.
  arXiv  Detail & Related papers  (2020-03-03T15:20:57Z)

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.