Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing

• URL: http://arxiv.org/abs/2011.08750v1
• Date: Tue, 17 Nov 2020 16:24:10 GMT
• Title: Iterative Semi-parametric Dynamics Model Learning For Autonomous Racing
• Authors: Ignat Georgiev, Christoforos Chatzikomis, Timo V\"olkl, Joshua Smith and Michael Mistry
• Abstract summary: We develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing. We show that our model can learn more accurately than a purely parametric model and generalize better than a purely non-parametric model.
• Score: 2.40966076588569
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurately modeling robot dynamics is crucial to safe and efficient motion control. In this paper, we develop and apply an iterative learning semi-parametric model, with a neural network, to the task of autonomous racing with a Model Predictive Controller (MPC). We present a novel non-linear semi-parametric dynamics model where we represent the known dynamics with a parametric model, and a neural network captures the unknown dynamics. We show that our model can learn more accurately than a purely parametric model and generalize better than a purely non-parametric model, making it ideal for real-world applications where collecting data from the full state space is not feasible. We present a system where the model is bootstrapped on pre-recorded data and then updated iteratively at run time. Then we apply our iterative learning approach to the simulated problem of autonomous racing and show that it can safely adapt to modified dynamics online and even achieve better performance than models trained on data from manual driving.

Related papers

• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Trajeglish: Traffic Modeling as Next-Token Prediction [67.28197954427638]
  A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. We apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Our model tops the Sim Agents Benchmark, surpassing prior work along the realism meta metric by 3.3% and along the interaction metric by 9.9%.
  arXiv  Detail & Related papers  (2023-12-07T18:53:27Z)
• 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)
• Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehicles [2.4779082385578337]
  We present control-oriented parametric models with varying levels of domain-awareness. We employ universal differential equations to construct data-driven blackbox and graybox representations of the AUV dynamics.
  arXiv  Detail & Related papers  (2022-08-15T17:01:14Z)
• Online Dynamics Learning for Predictive Control with an Application to Aerial Robots [3.673994921516517]
  Even though prediction models can be learned and applied to model-based controllers, these models are often learned offline. In this offline setting, training data is first collected and a prediction model is learned through an elaborated training procedure. We propose an online dynamics learning framework that continually improves the accuracy of the dynamic model during deployment.
  arXiv  Detail & Related papers  (2022-07-19T15:51:25Z)
• Gradient-Based Trajectory Optimization With Learned Dynamics [80.41791191022139]
  We use machine learning techniques to learn a differentiable dynamics model of the system from data. We show that a neural network can model highly nonlinear behaviors accurately for large time horizons. In our hardware experiments, we demonstrate that our learned model can represent complex dynamics for both the Spot and Radio-controlled (RC) car.
  arXiv  Detail & Related papers  (2022-04-09T22:07:34Z)
• 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)
• KNODE-MPC: A Knowledge-based Data-driven Predictive Control Framework for Aerial Robots [5.897728689802829]
  We make use of a deep learning tool, knowledge-based neural ordinary differential equations (KNODE), to augment a model obtained from first principles. The resulting hybrid model encompasses both a nominal first-principle model and a neural network learnt from simulated or real-world experimental data. To improve closed-loop performance, the hybrid model is integrated into a novel MPC framework, known as KNODE-MPC.
  arXiv  Detail & Related papers  (2021-09-10T12:09:18Z)
• Information Theoretic Model Predictive Q-Learning [64.74041985237105]
  We present a novel theoretical connection between information theoretic MPC and entropy regularized RL. We develop a Q-learning algorithm that can leverage biased models.
  arXiv  Detail & Related papers  (2019-12-31T00:29:22Z)

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.