Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics

• URL: http://arxiv.org/abs/2601.03813v1
• Date: Wed, 07 Jan 2026 11:18:29 GMT
• Title: Integrating Sample Inheritance into Bayesian Optimization for Evolutionary Robotics
• Abstract summary: In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms.<n>A common approach is to include controller optimization for each morphology, but starting from scratch for every new body may require a high controller learning budget.<n>We address this by using Bayesian optimization for controller optimization, exploiting its sample efficiency, and using sample inheritance as a form of Lamarckian inheritance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In evolutionary robotics, robot morphologies are designed automatically using evolutionary algorithms. This creates a body-brain optimization problem, where both morphology and control must be optimized together. A common approach is to include controller optimization for each morphology, but starting from scratch for every new body may require a high controller learning budget. We address this by using Bayesian optimization for controller optimization, exploiting its sample efficiency and strong exploration capabilities, and using sample inheritance as a form of Lamarckian inheritance. Under a deliberately low controller learning budget for each morphology, we investigate two types of sample inheritance: (1) transferring all the parent's samples to the offspring to be used as prior without evaluating them, and (2) reevaluating the parent's best samples on the offspring. Both are compared to a baseline without inheritance. Our results show that reevaluation performs best, with prior-based inheritance also outperforming no inheritance. Analysis reveals that while the learning budget is too low for a single morphology, generational inheritance compensates for this by accumulating learned adaptations across generations. Furthermore, inheritance mainly benefits offspring morphologies that are similar to their parents. Finally, we demonstrate the critical role of the environment, with more challenging environments resulting in more stable walking gaits. Our findings highlight that inheritance mechanisms can boost performance in evolutionary robotics without needing large learning budgets, offering an efficient path toward more capable robot design.

Related papers

• Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning [24.31239427177829]
  We show that morphological contributions to control learning decouple into two dimensions.<n>We propose AdaControl, which monitors disproportionate selection for morphological intelligence during evolution.<n>With AdaControl, a simple genetic algorithm rivals state-of-the-art generative-model-based co-design methods in discovering diverse high-performing designs.
  arXiv  Detail & Related papers  (2026-08-24T11:06:24Z)
• Lamarckian Inheritance in Dynamic Environments: How Key Variables Affect Evolutionary Dynamics [0.0]
  We show that the benefit of Lamarckian inheritance depends on two variables: how conflicting the environmental changes are to robot control, and the predictability of those changes for the robotic agent.<n>We find that adding a sensor to detect environmental changes restores the benefits for Lamarckian inheritance in conflicting environments.
  arXiv  Detail & Related papers  (2026-05-15T09:26:40Z)
• Generational Replacement and Learning for High-Performing and Diverse Populations in Evolvable Robots [0.0]
  We show that combining generational replacement with intra-life learning can increase diversity while retaining performance.<n>We also highlight the importance of performance metrics when studying learning in morphologically evolving robots.
  arXiv  Detail & Related papers  (2026-01-07T11:11:57Z)
• Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity [0.39945675027960637]
  We introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling.<n>GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines.<n>We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness.
  arXiv  Detail & Related papers  (2025-04-22T20:34:47Z)
• On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness [15.656614304616006]
  We introduce AdamBO, a single-loop Adam-type method that achieves $wide.<n>We conduct experiments on various machine learning tasks involving bilevel.<n> formulations with recurrent neural networks (RNNs) and transformers.
  arXiv  Detail & Related papers  (2025-03-05T21:16:59Z)
• Neural Lineage [56.34149480207817]
  We introduce a novel task known as neural lineage detection, aiming at discovering lineage relationships between parent and child models. For practical convenience, we introduce a learning-free approach, which integrates an approximation of the finetuning process into the neural network representation similarity metrics. For the pursuit of accuracy, we introduce a learning-based lineage detector comprising encoders and a transformer detector.
  arXiv  Detail & Related papers  (2024-06-17T01:11:53Z)
• End-to-End Meta-Bayesian Optimisation with Transformer Neural Processes [52.818579746354665]
  This paper proposes the first end-to-end differentiable meta-BO framework that generalises neural processes to learn acquisition functions via transformer architectures. We enable this end-to-end framework with reinforcement learning (RL) to tackle the lack of labelled acquisition data.
  arXiv  Detail & Related papers  (2023-05-25T10:58:46Z)
• Meta-Auxiliary Learning for Adaptive Human Pose Prediction [26.877194503491072]
  Predicting high-fidelity future human poses is decisive for intelligent robots to interact with humans. Deep end-to-end learning approaches, which typically train a generic pre-trained model on external datasets and then directly apply it to all test samples, remain non-optimal. We propose a novel test-time adaptation framework that leverages two self-supervised auxiliary tasks to help the primary forecasting network adapt to the test sequence.
  arXiv  Detail & Related papers  (2023-04-13T11:17:09Z)
• Metric Residual Networks for Sample Efficient Goal-conditioned Reinforcement Learning [52.59242013527014]
  Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications. Sample efficiency is of utmost importance for GCRL since, by default, the agent is only rewarded when it reaches its goal. We introduce a novel neural architecture for GCRL that achieves significantly better sample efficiency than the commonly-used monolithic network architecture.
  arXiv  Detail & Related papers  (2022-08-17T08:04:41Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• Morpho-evolution with learning using a controller archive as an inheritance mechanism [4.364091192392204]
  We propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. By inheriting an appropriate controller from an archive rather than learning from a randomly initialised one, we show that both the speed and magnitude of learning increases over time. The framework also provides new insights into the complex interactions between evolution and learning, and the role of morphological intelligence in robot design.
  arXiv  Detail & Related papers  (2021-04-09T09:32:36Z)
• 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)
• AdaLead: A simple and robust adaptive greedy search algorithm for sequence design [55.41644538483948]
  We develop an easy-to-directed, scalable, and robust evolutionary greedy algorithm (AdaLead) AdaLead is a remarkably strong benchmark that out-competes more complex state of the art approaches in a variety of biologically motivated sequence design challenges.
  arXiv  Detail & Related papers  (2020-10-05T16:40:38Z)

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.