# Data-efficient Learning of Morphology and Controller for a Microrobot

arXiv: https://arxiv.org/abs/1905.01334 · HTML: https://mlsanity.com/paper/1905.01334/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

## Built on by (most cited first, 26 of 26)

- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [MetaMorph: Learning Universal Controllers with Transformers](https://mlsanity.com/paper/2203.11931/) (2022)
- [Conservative Objective Models for Effective Offline Model-Based Optimization](https://mlsanity.com/paper/2107.06882/) (2021)
- [Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots](https://mlsanity.com/paper/2201.09863/) (2022)
- [RoMA: Robust Model Adaptation for Offline Model-based Optimization](https://mlsanity.com/paper/2110.14188/) (2021)
- [LICO: Large Language Models for In-Context Molecular Optimization](https://mlsanity.com/paper/2406.18851/) (2024)
- [ExPT: Synthetic Pretraining for Few-Shot Experimental Design](https://mlsanity.com/paper/2310.19961/) (2023)
- [Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization](https://mlsanity.com/paper/2407.01624/) (2024)
- [The Effects of Learning in Morphologically Evolving Robot Systems](https://mlsanity.com/paper/2111.09851/) (2021)
- [Design Editing for Offline Model-based Optimization](https://mlsanity.com/paper/2405.13964/) (2024)
- [VLMgineer: Vision Language Models as Robotic Toolsmiths](https://mlsanity.com/paper/2507.12644/) (2025)
- [Morpho-evolution with learning using a controller archive as an inheritance mechanism](https://mlsanity.com/paper/2104.04269/) (2021)
- [MORPH: Design Co-optimization with Reinforcement Learning via a Differentiable Hardware Model Proxy](https://mlsanity.com/paper/2309.17227/) (2023)
- [Learning to Design and Use Tools for Robotic Manipulation](https://mlsanity.com/paper/2311.00754/) (2023)
- [Efficient Hyperparameter Optimization for Physics-based Character Animation](https://mlsanity.com/paper/2104.12365/) (2021)
- [MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation](https://mlsanity.com/paper/2203.16912/) (2022)
- [Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution](https://mlsanity.com/paper/2309.12529/) (2023)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [Co-Imitation: Learning Design and Behaviour by Imitation](https://mlsanity.com/paper/2209.01207/) (2022)
- [What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks](https://mlsanity.com/paper/2111.02371/) (2021)
- [CURE: Simulation-Augmented Auto-Tuning in Robotics](https://mlsanity.com/paper/2402.05399/) (2024)
- [ROMO: Retrieval-enhanced Offline Model-based Optimization](https://mlsanity.com/paper/2310.07560/) (2023)
- [From Function to Distribution Modeling: A PAC-Generative Approach to Offline Optimization](https://mlsanity.com/paper/2401.02019/) (2024)
- [An adaptive approach to Bayesian Optimization with switching costs](https://mlsanity.com/paper/2405.08973/) (2024)
- [An Empirical Study on the Computation Budget of Co-Optimization of Robot Design and Control in Simulation](https://mlsanity.com/paper/2409.08621/) (2024)
- [Few-Shot Design Optimization by Exploiting Auxiliary Information](https://mlsanity.com/paper/2602.12112/) (2026)

## Abstract (arXiv)

Robot design is often a slow and difficult process requiring the iterative construction and testing of prototypes, with the goal of sequentially optimizing the design. For most robots, this process is further complicated by the need, when validating the capabilities of the hardware to solve the desired task, to already have an appropriate controller, which is in turn designed and tuned for the specific hardware. In this paper, we propose a novel approach, HPC-BBO, to efficiently and automatically design hardware configurations, and evaluate them by also automatically tuning the corresponding controller. HPC-BBO is based on a hierarchical Bayesian optimization process which iteratively optimizes morphology configurations (based on the performance of the previous designs during the controller learning process) and subsequently learns the corresponding controllers (exploiting the knowledge collected from optimizing for previous morphologies). Moreover, HPC-BBO can select a "batch" of multiple morphology designs at once, thus parallelizing hardware validation and reducing the number of time-consuming production cycles. We validate HPC-BBO on the design of the morphology and controller for a simulated 6-legged microrobot. Experimental results show that HPC-BBO outperforms multiple competitive baselines, and yields a 360% reduction in production cycles over standard Bayesian optimization, thus reducing the hypothetical manufacturing time of our microrobot from 21 to 4 months.