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Meta-Learning for Rapid Adaptation in Reference Tra... | AI Research

Key Takeaways

  • Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems This paper introduces a meta-learning framework designed to help cont...
  • In this paper, we address the problem of reference tracking for uncertain nonlinear systems.
  • Since collecting data from the target system (i.e., the system of interest) is often challenging, our objective is to design optimal controllers using limited target system data.
  • Meta-learning provides a promising paradigm by leveraging offline data from source systems (systems sharing structural similarities with the target system) to accelerate training and enhance control performance.
  • Motivated by this idea, we propose a meta-learning-based control framework that tailors the implicit model-agnostic meta-learning (iMAML) algorithm to the control setting.
Paper AbstractExpand

In this paper, we address the problem of reference tracking for uncertain nonlinear systems. Since collecting data from the target system (i.e., the system of interest) is often challenging, our objective is to design optimal controllers using limited target system data. Meta-learning provides a promising paradigm by leveraging offline data from source systems (systems sharing structural similarities with the target system) to accelerate training and enhance control performance. Motivated by this idea, we propose a meta-learning-based control framework that tailors the implicit model-agnostic meta-learning (iMAML) algorithm to the control setting. The framework operates in two phases: an (offline) meta-training phase, where an aggregated representation is learned from source data to capture the shared system dynamics among similar systems, and an (online) meta-adaptation phase, where this representation is fine-tuned on the target system using only a few data samples and limited adaptation steps. We formulate this framework as a bi-level optimization problem and provide an efficient solution with reduced storage complexity and few approximations. The proposed framework is general, allowing various learning algorithms to be integrated. To demonstrate this flexibility, we propose two specific learning algorithms that can be incorporated into our framework based on a neural state-space model and a deep Q-network, respectively. The primary distinction between these approaches is whether explicit system identification is required. Numerical simulations and hardware experiments demonstrate that the proposed methods enhance control performance and consistently outperform baseline approaches.

Meta-Learning for Rapid Adaptation in Reference Tracking of Uncertain Nonlinear Systems
This paper introduces a meta-learning framework designed to help control systems adapt quickly to new, uncertain environments. In many real-world applications like robotics or autonomous driving, collecting enough data to train a controller from scratch is expensive, time-consuming, or unsafe. This research addresses that challenge by using "meta-learning," which allows a controller to learn from historical data across several similar systems. By pre-training on these source systems, the controller gains a flexible foundation that can be fine-tuned for a specific target system using only a small amount of new data.

A Two-Phase Learning Approach

The framework operates in two distinct stages. First, in the offline meta-training phase, the system analyzes data from multiple source systems to learn an "aggregated representation." This representation captures the shared structural dynamics common to these systems. Second, in the online meta-adaptation phase, this pre-trained model is deployed to the target system. Because the model already understands the general dynamics, it requires only a few data samples and a limited number of adaptation steps to achieve high performance on the specific target task.

Solving the Optimization Challenge

To make this process efficient, the authors utilize a technique called implicit Model-Agnostic Meta-Learning (iMAML). Standard meta-learning approaches often struggle with memory and computational costs because they must track every step of the adaptation process during training. By using the implicit function theorem, this framework avoids the need to store the entire optimization path. Instead, it calculates the necessary updates based only on the final state of the model. This significantly reduces memory usage and allows for more accurate gradient computations, leading to better overall control performance.

Flexibility Across Control Methods

A key strength of this framework is its versatility. It is designed to be "model-agnostic," meaning it is not tied to one specific way of controlling a system. The authors demonstrate this by integrating two different approaches:

  • Indirect data-driven control: This method first identifies the system's model and then designs a controller based on that model.

  • Direct data-driven control: This method optimizes the control policy directly from the data without needing an explicit model.
    By providing a unified mathematical structure, the framework allows practitioners to swap in different learning algorithms depending on the specific needs of their application.

Performance and Real-World Impact

Numerical simulations and hardware experiments—specifically using a ball-on-a-plate system—show that this meta-learning approach consistently outperforms traditional baseline methods. By pre-training in a simulated environment, the system effectively bridges the "sim-to-real" gap, allowing the controller to perform well on physical hardware with minimal real-world training. This makes the approach particularly useful for industries where rapid, data-efficient adaptation is critical for maintaining stable and accurate control in changing environments.

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