Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control

• URL: http://arxiv.org/abs/2210.12583v4
• Date: Sat, 31 Aug 2024 14:15:27 GMT
• Title: Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control
• Authors: Alessandro Saviolo, Jonathan Frey, Abhishek Rathod, Moritz Diehl, Giuseppe Loianno,
• Abstract summary: We present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions.
• Score: 46.81433026280051
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Model-based control requires an accurate model of the system dynamics for precisely and safely controlling the robot in complex and dynamic environments. Moreover, in the presence of variations in the operating conditions, the model should be continuously refined to compensate for dynamics changes. In this paper, we present a self-supervised learning approach that actively models the dynamics of nonlinear robotic systems. We combine offline learning from past experience and online learning from current robot interaction with the unknown environment. These two ingredients enable a highly sample-efficient and adaptive learning process, capable of accurately inferring model dynamics in real-time even in operating regimes that greatly differ from the training distribution. Moreover, we design an uncertainty-aware model predictive controller that is heuristically conditioned to the aleatoric (data) uncertainty of the learned dynamics. This controller actively chooses the optimal control actions that (i) optimize the control performance and (ii) improve the efficiency of online learning sample collection. We demonstrate the effectiveness of our method through a series of challenging real-world experiments using a quadrotor system. Our approach showcases high resilience and generalization capabilities by consistently adapting to unseen flight conditions, while it significantly outperforms classical and adaptive control baselines.

Related papers

• Learning Exactly Linearizable Deep Dynamics Models [0.07366405857677226]
  We propose a learning method for exactly linearizable dynamical models that can easily apply various control theories to ensure stability, reliability, etc. The proposed model is employed for the real-time control of an automotive engine, and the results demonstrate good predictive performance and stable control under constraints.
  arXiv  Detail & Related papers  (2023-11-30T05:40:55Z)
• Data-Driven Control with Inherent Lyapunov Stability [3.695480271934742]
  We propose Control with Inherent Lyapunov Stability (CoILS) as a method for jointly learning parametric representations of a nonlinear dynamics model and a stabilizing controller from data. In addition to the stabilizability of the learned dynamics guaranteed by our novel construction, we show that the learned controller stabilizes the true dynamics under certain assumptions on the fidelity of the learned dynamics.
  arXiv  Detail & Related papers  (2023-03-06T14:21:42Z)
• ControlVAE: Model-Based Learning of Generative Controllers for Physics-Based Characters [28.446959320429656]
  We introduce ControlVAE, a model-based framework for learning generative motion control policies based on variational autoencoders (VAE) Our framework can learn a rich and flexible latent representation of skills and a skill-conditioned generative control policy from a diverse set of unorganized motion sequences. We demonstrate the effectiveness of ControlVAE using a diverse set of tasks, which allows realistic and interactive control of the simulated characters.
  arXiv  Detail & Related papers  (2022-10-12T10:11:36Z)
• 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)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)
• 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)
• Adaptive Model Predictive Control by Learning Classifiers [26.052368583196426]
  We propose an adaptive MPC variant that automatically estimates control and model parameters. We leverage recent results showing that BO can be formulated as a density ratio estimation. This is then integrated into a model predictive path integral control framework yielding robust controllers for a variety of challenging robotics tasks.
  arXiv  Detail & Related papers  (2022-03-13T23:22:12Z)
• Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning [137.39196753245105]
  We present a new model-based reinforcement learning algorithm that learns a multi-headed dynamics model for dynamics generalization. We incorporate context learning, which encodes dynamics-specific information from past experiences into the context latent vector. Our method exhibits superior zero-shot generalization performance across a variety of control tasks, compared to state-of-the-art RL methods.
  arXiv  Detail & Related papers  (2020-10-26T03:20:42Z)
• Anticipating the Long-Term Effect of Online Learning in Control [75.6527644813815]
  AntLer is a design algorithm for learning-based control laws that anticipates learning. We show that AntLer approximates an optimal solution arbitrarily accurately with probability one.
  arXiv  Detail & Related papers  (2020-07-24T07:00:14Z)
• 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.