Structured Mechanical Models for Robot Learning and Control

• URL: http://arxiv.org/abs/2004.10301v1
• Date: Tue, 21 Apr 2020 21:12:03 GMT
• Title: Structured Mechanical Models for Robot Learning and Control
• Authors: Jayesh K. Gupta, Kunal Menda, Zachary Manchester and Mykel J. Kochenderfer
• Abstract summary: Black-box neural networks suffer from data-inefficiency and the difficulty to incorporate prior knowledge. We introduce Structured Mechanical Models that are data-efficient, easily amenable to prior knowledge, and easily usable with model-based control techniques. We demonstrate that they generalize better from limited data and yield more reliable model-based controllers on a variety of simulated robotic domains.
• Score: 38.52004843488286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Model-based methods are the dominant paradigm for controlling robotic systems, though their efficacy depends heavily on the accuracy of the model used. Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge. We introduce Structured Mechanical Models, a flexible model class for mechanical systems that are data-efficient, easily amenable to prior knowledge, and easily usable with model-based control techniques. The goal of this work is to demonstrate the benefits of using Structured Mechanical Models in lieu of black-box neural networks when modeling robot dynamics. We demonstrate that they generalize better from limited data and yield more reliable model-based controllers on a variety of simulated robotic domains.

Related papers

• Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control [2.058941610795796]
  This paper introduces a streamlined method for learning low-dimensional, physics-based models. We validate our approach through simulations with various planar soft manipulators. Thanks to the capability of the method of generating physically compatible models, the learned models can be straightforwardly combined with model-based control policies.
  arXiv  Detail & Related papers  (2024-10-31T18:37:22Z)
• Comparative Evaluation of Learning Models for Bionic Robots: Non-Linear Transfer Function Identifications [0.0]
  The control and modeling of robot dynamics have increasingly adopted model-free control strategies using machine learning. This research introduces a comprehensive evaluation strategy and framework for the application of model-free control.
  arXiv  Detail & Related papers  (2024-07-02T17:00:23Z)
• 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)
• DST: Dynamic Substitute Training for Data-free Black-box Attack [79.61601742693713]
  We propose a novel dynamic substitute training attack method to encourage substitute model to learn better and faster from the target model. We introduce a task-driven graph-based structure information learning constrain to improve the quality of generated training data.
  arXiv  Detail & Related papers  (2022-04-03T02:29:11Z)
• Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms [59.03426963238452]
  We present Real-time Neural MPC, a framework to efficiently integrate large, complex neural network architectures as dynamics models within a model-predictive control pipeline. We show the feasibility of our framework on real-world problems by reducing the positional tracking error by up to 82% when compared to state-of-the-art MPC approaches without neural network dynamics.
  arXiv  Detail & Related papers  (2022-03-15T09:38:15Z)
• Constructing Neural Network-Based Models for Simulating Dynamical Systems [59.0861954179401]
  Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. This paper provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome.
  arXiv  Detail & Related papers  (2021-11-02T10:51:42Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective [5.597839822252915]
  We study supervised regression models that combine rigid-body mechanics with data-driven modelling techniques. We provide a unified view on the combination of data-driven regression models, such as neural networks and Gaussian processes, with analytical model priors.
  arXiv  Detail & Related papers  (2020-12-11T11:26:48Z)
• Modeling System Dynamics with Physics-Informed Neural Networks Based on Lagrangian Mechanics [3.214927790437842]
  Two main modeling approaches often fail to meet requirements: first principles methods suffer from high bias, whereas data-driven modeling tends to have high variance. We present physics-informed neural ordinary differential equations (PINODE), a hybrid model that combines the two modeling techniques to overcome the aforementioned problems. Our findings are of interest for model-based control and system identification of mechanical systems.
  arXiv  Detail & Related papers  (2020-05-29T15:10:43Z)

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.