- The paper introduces an algorithm that incrementally exposes ANN controllers to morphological variations to create robust, generalist controllers.
- The methodology employs evolutionary strategies with adaptive branching across tasks like CartPole and Ant, revealing a specialization-generalization trade-off.
- Experimental results emphasize the importance of incremental training schedules in achieving enhanced control performance across diverse morphologies.
Evolving Generalist Controllers to Handle a Wide Range of Morphological Variations
Abstract Summary: The paper addresses the challenge of evolving artificial neural network (ANN) controllers that are robust and generalizable across a variety of morphological variations. Such capabilities are particularly desirable in fields like robotics, where unexpected changes in morphology or environment could otherwise compromise performance. The authors propose an algorithm that introduces morphological variations during the evolutionary process to evolve ANN controllers capable of managing a broad range of morphologies without requiring explicit adaptation. The proposed approach was validated through extensive experiments on different simulation environments, revealing a trade-off between specialization and generalization.
Introduction
Evolving robust and generalizable controllers is crucial for applications that demand stability amidst unforeseen morphological changes. This research leverages the power of Evolution Strategies (ES) to develop ANNs that can tackle continuous control problems effectively. The authors argue that evolved controllers might often fail to cope with unexpected variations if designed for specific tasks or environments, which poses risks in real-world applications.
Advancing the understanding of ANN robustness and generalization remains a key challenge, as specialist controllers tend to overfit to their specific morphologies. Instead, the ability to handle a wider spectrum of morphological variations can significantly enhance robustness. This paper builds upon prior work demonstrating ES effectiveness in agent behavior learning, proposing enhancements to foster generalist controller evolution.
Methodology
The proposed algorithm aims to cultivate ANN controllers that can generalize their performance across various morphologies. The process involves evolving controllers with ES while gradually introducing morphological variations. The authors crafted a training schedule to guide this evolutionary approach, hypothesizing that exposure to a diverse set of morphologies during the training phase would yield more adaptable controllers.
Specifically, the authors explore a multi-controller strategy resembling evolutionary branching, where a population of controllers adapts to specific partitions of the morphology space. This adaptive branching results in specialized subsets that allow different controllers to efficiently manage sections of morphological variations.
Experimental Setup
Experiments were conducted using four control tasks from the OpenAI gym: CartPole, Bipedal Walker, Ant, and Walker2D. Morphological variations were introduced in different sets, and multiple training schedules were tested to evaluate the algorithm's effectiveness.
Neuro-evolutionary experiments involved fully-connected feedforward ANN topologies with task-specific configurations based on input, hidden, and output layers. xNES (Exponential Natural Evolution Strategies) was employed for optimization due to its strengths in high-dimensional, continuous problem domains. Performance metrics for evaluation included the default morphology, local variations, and global variations sets.
Results and Analysis
The paper found that larger sets of training morphologies led to the evolution of more robust and generalist controllers, with an evident trade-off: while performance on specific morphologies (like the default) was diminished, robustness and generalizability were substantially enhanced when faced with morphological variances.
In simpler tasks, like CartPole, no evolutionary branching occurred due to task simplicity. More complex tasks, such as Bipedal Walker and Ant, demonstrated the utility of evolutionary branching, producing multiple controllers each adept at handling specific morphological partitions. This partitioning reflects the natural evolutionary branching concept and enhances control robustness across broader morphology spaces.
The incremental training schedule outperformed others in creating generalist controllers, highlighting the importance of the order of morphological exposure during training.
Implications and Future Directions
This paper advances ANN-based control strategies by emphasizing the indispensability of generalization. Its algorithmic framework offers a foundation to develop controllers less susceptible to failure despite morphological changes, thus aligning closer to real-world applicability.
Future research could extend the algorithm's capabilities in real-world scenarios, possibly including non-uniform morphological changes and other variation types. Additionally, integrating methods to automatically scale fitness evaluations relative to morphology might enhance fairness in controller performance assessments.
The adaptation of this methodology to various ANN architectures, or coupling with existent methods like regularization and curriculum learning, could further amplify robustness and generalization, offering promising directions for continued investigation in robotic control systems.