# Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects

arXiv: https://arxiv.org/abs/2012.12209 · HTML: https://mlsanity.com/paper/2012.12209/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2020.

## Built on by (most cited first, 11 of 11)

- [An End-to-End Differentiable Framework for Contact-Aware Robot Design](https://mlsanity.com/paper/2107.07501/) (2021)
- [GLSO: Grammar-guided Latent Space Optimization for Sample-efficient Robot Design Automation](https://mlsanity.com/paper/2209.11748/) (2022)
- [REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer](https://mlsanity.com/paper/2202.05244/) (2022)
- [Learning Tool Morphology for Contact-Rich Manipulation Tasks with Differentiable Simulation](https://mlsanity.com/paper/2211.02201/) (2022)
- [BodyGen: Advancing Towards Efficient Embodiment Co-Design](https://mlsanity.com/paper/2503.00533/) (2025)
- [HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration](https://mlsanity.com/paper/2212.04359/) (2022)
- [Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges](https://mlsanity.com/paper/2512.04770/) (2025)
- [What Robot do I Need? Fast Co-Adaptation of Morphology and Control using Graph Neural Networks](https://mlsanity.com/paper/2111.02371/) (2021)
- [MISCGrasp: Leveraging Multiple Integrated Scales and Contrastive Learning for Enhanced Volumetric Grasping](https://mlsanity.com/paper/2507.02672/) (2025)
- [Latent Diffeomorphic Co-Design of End-Effectors for Deformable and Fragile Object Manipulation](https://mlsanity.com/paper/2602.17921/) (2026)
- [RobotDesignGPT: Automated Robot Design Synthesis using Vision Language Models](https://mlsanity.com/paper/2601.11801/) (2026)

## Abstract (arXiv)

Evolution in nature illustrates that the creatures' biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to morph and acquire new skills can facilitate an embodied agent to solve tasks of varying complexities. In this work, we introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. Jointly optimizing morphology and control imposes computational challenges since it requires constant evaluation of a black-box function that measures the performance of a combination of embodiment and behavior. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills jointly through learned latent-space representations. We design the grasping tasks based on a taxonomy of three human grasp types: power grasp, pinch grasp, and lateral grasp. Through experimentation and comparative study, we demonstrate the effectiveness of our approach in discovering robust and cost-efficient hand morphologies for grasping novel objects.