Evolving generalist controllers to handle a wide range of morphological variations

• URL: http://arxiv.org/abs/2309.10201v4
• Date: Fri, 19 Jul 2024 03:19:57 GMT
• Title: Evolving generalist controllers to handle a wide range of morphological variations
• Authors: Corinna Triebold, Anil Yaman,
• Abstract summary: The study of robustness and generalizability of artificial neural networks (ANNs) has remained limited. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalizability of the controllers.
• Score: 1.4425878137951238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neuro-evolutionary methods have proven effective in addressing a wide range of tasks. However, the study of the robustness and generalizability of evolved artificial neural networks (ANNs) has remained limited. This has immense implications in the fields like robotics where such controllers are used in control tasks. Unexpected morphological or environmental changes during operation can risk failure if the ANN controllers are unable to handle these changes. This paper proposes an algorithm that aims to enhance the robustness and generalizability of the controllers. This is achieved by introducing morphological variations during the evolutionary training process. As a results, it is possible to discover generalist controllers that can handle a wide range of morphological variations sufficiently without the need of the information regarding their morphologies or adaptation of their parameters. We perform an extensive experimental analysis on simulation that demonstrates the trade-off between specialist and generalist controllers. The results show that generalists are able to control a range of morphological variations with a cost of underperforming on a specific morphology relative to a specialist. This research contributes to the field by addressing the limited understanding of robustness and generalizability and proposes a method by which to improve these properties.

Related papers

• The Effect of Training Schedules on Morphological Robustness and Generalization [11.068618445120508]
  Robustness and generalizability are key properties of artificial neural network (ANN)-based controllers. In this paper, we focus on morphological robustness and generalizability concerned with finding an ANN-based controller that can provide sufficient performance on a range of physical variations.
  arXiv  Detail & Related papers  (2024-07-19T00:55:31Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Universal Morphology Control via Contextual Modulation [52.742056836818136]
  Learning a universal policy across different robot morphologies can significantly improve learning efficiency and generalization in continuous control. Existing methods utilize graph neural networks or transformers to handle heterogeneous state and action spaces across different morphologies. We propose a hierarchical architecture to better model this dependency via contextual modulation.
  arXiv  Detail & Related papers  (2023-02-22T00:04:12Z)
• DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body [126.52031472297413]
  We introduce DMAP, a biologically-inspired, attention-based policy network architecture. We show that a control policy based on the proprioceptive state performs poorly with highly variable body configurations. DMAP can be trained end-to-end in all the considered environments, overall matching or surpassing the performance of an oracle agent.
  arXiv  Detail & Related papers  (2022-09-28T16:45:35Z)
• The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness [0.0]
  We introduce a method that permits us to measure the impact of morphological variations. We analyze the relation between the amplitude of variations, the modality with which they are introduced, and the performance and robustness of evolving agents. Our results show that morphological variations permit generating solutions which perform better in varying and non-varying conditions.
  arXiv  Detail & Related papers  (2022-08-04T17:58:15Z)
• On the Generalization and Adaption Performance of Causal Models [99.64022680811281]
  Differentiable causal discovery has proposed to factorize the data generating process into a set of modules. We study the generalization and adaption performance of such modular neural causal models. Our analysis shows that the modular neural causal models outperform other models on both zero and few-shot adaptation in low data regimes.
  arXiv  Detail & Related papers  (2022-06-09T17:12:32Z)
• Adaptation through prediction: multisensory active inference torque control [0.0]
  We present a novel multisensory active inference torque controller for industrial arms. Our controller, inspired by the predictive brain hypothesis, improves the capabilities of current active inference approaches.
  arXiv  Detail & Related papers  (2021-12-13T16:03:18Z)
• Redefining Neural Architecture Search of Heterogeneous Multi-Network Models by Characterizing Variation Operators and Model Components [71.03032589756434]
  We investigate the effect of different variation operators in a complex domain, that of multi-network heterogeneous neural models. We characterize both the variation operators, according to their effect on the complexity and performance of the model; and the models, relying on diverse metrics which estimate the quality of the different parts composing it.
  arXiv  Detail & Related papers  (2021-06-16T17:12:26Z)
• Task-Agnostic Morphology Evolution [94.97384298872286]
  Current approaches that co-adapt morphology and behavior use a specific task's reward as a signal for morphology optimization. This often requires expensive policy optimization and results in task-dependent morphologies that are not built to generalize. We propose a new approach, Task-Agnostic Morphology Evolution (TAME), to alleviate both of these issues.
  arXiv  Detail & Related papers  (2021-02-25T18:59:21Z)

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.