Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation

• URL: http://arxiv.org/abs/2103.06615v5
• Date: Mon, 8 May 2023 12:25:58 GMT
• Title: Controlled Gaussian Process Dynamical Models with Application to Robotic Cloth Manipulation
• Authors: Fabio Amadio, Juan Antonio Delgado-Guerrero, Adri\`a Colom\'e and Carme Torras
• Abstract summary: We propose Controlled Gaussian Process Dynamical Model (CGPDM) for learning high-dimensional, nonlinear dynamics. CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act. It is capable of generalizing over a wide range of movements and confidently predicting the cloth motions obtained by previously unseen sequences of control actions.
• Score: 10.04778213256535
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Over the last years, significant advances have been made in robotic manipulation, but still, the handling of non-rigid objects, such as cloth garments, is an open problem. Physical interaction with non-rigid objects is uncertain and complex to model. Thus, extracting useful information from sample data can considerably improve modeling performance. However, the training of such models is a challenging task due to the high-dimensionality of the state representation. In this paper, we propose Controlled Gaussian Process Dynamical Model (CGPDM) for learning high-dimensional, nonlinear dynamics by embedding it in a low-dimensional manifold. A CGPDM is constituted by a low-dimensional latent space, with an associated dynamics where external control variables can act and a mapping to the observation space. The parameters of both maps are marginalized out by considering Gaussian Process (GP) priors. Hence, a CGPDM projects a high-dimensional state space into a smaller dimension latent space, in which it is feasible to learn the system dynamics from training data. The modeling capacity of CGPDM has been tested in both a simulated and a real scenario, where it proved to be capable of generalizing over a wide range of movements and confidently predicting the cloth motions obtained by previously unseen sequences of control actions.

Related papers

• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models [11.13664702335756]
  We propose to integrate the efficient transformed Gaussian process (ETGP) into the GPSSM to efficiently model the transition function in high-dimensional latent state space. We also develop a corresponding variational inference algorithm that surpasses existing methods in terms of parameter count and computational complexity.
  arXiv  Detail & Related papers  (2023-09-03T04:34:33Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• Data-driven low-dimensional dynamic model of Kolmogorov flow [0.0]
  Reduced order models (ROMs) that capture flow dynamics are of interest for decreasing computational costs for simulation. This work presents a data-driven framework for minimal-dimensional models that effectively capture the dynamics and properties of the flow. We apply this to Kolmogorov flow in a regime consisting of chaotic and intermittent behavior.
  arXiv  Detail & Related papers  (2022-10-29T23:05:39Z)
• Deep Kernel Learning of Dynamical Models from High-Dimensional Noisy Data [1.3750624267664155]
  The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables. The training of the proposed model is carried out in an unsupervised manner, not relying on labeled data. Results show that the method can effectively denoise measurements, learn compact state representations and latent dynamical models.
  arXiv  Detail & Related papers  (2022-08-27T09:47:44Z)
• Data-driven Control of Agent-based Models: an Equation/Variable-free Machine Learning Approach [0.0]
  We present an Equation/Variable free machine learning (EVFML) framework for the control of the collective dynamics of complex/multiscale systems. The proposed implementation consists of three steps: (A) from high-dimensional agent-based simulations, machine learning (in particular, non-linear manifold learning (DMs)) We exploit the Equation-free approach to perform numerical bifurcation analysis of the emergent dynamics. We design data-driven embedded wash-out controllers that drive the agent-based simulators to their intrinsic, imprecisely known, emergent open-loop unstable steady-states.
  arXiv  Detail & Related papers  (2022-07-12T18:16:22Z)
• ACID: Action-Conditional Implicit Visual Dynamics for Deformable Object Manipulation [135.10594078615952]
  We introduce ACID, an action-conditional visual dynamics model for volumetric deformable objects. A benchmark contains over 17,000 action trajectories with six types of plush toys and 78 variants. Our model achieves the best performance in geometry, correspondence, and dynamics predictions.
  arXiv  Detail & Related papers  (2022-03-14T04:56:55Z)
• STAR: Sparse Transformer-based Action Recognition [61.490243467748314]
  This work proposes a novel skeleton-based human action recognition model with sparse attention on the spatial dimension and segmented linear attention on the temporal dimension of data. Experiments show that our model can achieve comparable performance while utilizing much less trainable parameters and achieve high speed in training and inference.
  arXiv  Detail & Related papers  (2021-07-15T02:53:11Z)
• Dynamic Mode Decomposition in Adaptive Mesh Refinement and Coarsening Simulations [58.720142291102135]
  Dynamic Mode Decomposition (DMD) is a powerful data-driven method used to extract coherent schemes. This paper proposes a strategy to enable DMD to extract from observations with different mesh topologies and dimensions.
  arXiv  Detail & Related papers  (2021-04-28T22:14:25Z)
• GEM: Group Enhanced Model for Learning Dynamical Control Systems [78.56159072162103]
  We build effective dynamical models that are amenable to sample-based learning. We show that learning the dynamics on a Lie algebra vector space is more effective than learning a direct state transition model. This work sheds light on a connection between learning of dynamics and Lie group properties, which opens doors for new research directions.
  arXiv  Detail & Related papers  (2021-04-07T01:08:18Z)
• Prediction with Approximated Gaussian Process Dynamical Models [7.678864239473703]
  We present approximated GPDMs which are Markov and analyze their control theoretical properties. The outcomes are illustrated with numerical examples that show the power of the approximated models.
  arXiv  Detail & Related papers  (2020-06-25T16:51:17Z)

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.