Human bodies have been carefully refined through the long process of evolution, enabling us to utilize our bodies to solve a multitude of tasks skillfully. This capability, natural to humans, remains challenging for embodied agents, such as robots. The difficulty arises because successful interactions are highly dependent on the synergy between brain, body, and environment. Recent years, the concept of Brain-Body Co-Design (BBCD) has garnered growing cross-disciplinary research interest. Unlike traditional approaches to embodied agent design, which focus primarily on optimizing an agent’s controlling brain within a fixed body morphology, BBCD highlights the concurrent optimization of both elements, leading agents to superior adaptability and task performance.
🔑 Contributions to Embodied AI: To the best of our knowledge, this survey is the first attempt to survey Brain-Body Co-Design approaches in the context of Embodied AI. We envision this survey as an essential starting point for researchers from different academic backgrounds entering this exciting and rapidly evolving field.
2025/01/01: Happy New Year! I am updating the survey on this topic and will fully update the site when the survey is updated to Arxiv.
Feel free to pull requests or contact us if you find any related papers that are not included here. The process to submit a pull request is as follows:
- a. Fork the project into your own repository.
- b. Add the Title, Paper link, Conference, Project/Code link in
README.mdusing the following format:
|[Title](Paper Link)|Conference|Agent Type|[Code/Project](Code/Project link)|- c. Submit the pull request to this branch.
- We will update this page on a regular basis! So stay tuned~ 🎉🎉🎉. If you do find our survey or the repository helpful, please consider kindly giving a ⭐. 谢谢你, Thanks a lot, Спасибо, ありがとう, 감사합니다, Merci, Grazie, Obrigado, Danke, شكراً
In this survey, we systematically investigate recent progress in BBCD across simulated and real-world scenarios. We begin by defining the BBCD problem and positioning it with respect to related fields. Subsequently, we propose a novel framework for categorizing the state-of-the-art BBCD approaches, under which we analyze their co-design spaces, methodologies, benchmarks, and applications. Finally, we highlight the current challenges in the field and provide insights for potentially interesting future directions.
TODO:::
In general, a robot’s ability to learn a task depends on three major system components, namely, the body (morphology, hardware), the brain (controller, software), and the learning algorithm. TODO:::
- 🚀 Brain-Body Co-Design of Embodied Agents: A Survey
- 📋 Update List
- 🔥 Comments
- 🎥 Contents
- ⭐ Bi-Level Co-Design Methods
- ⭐ End-to-end Co-Design
- Brain-Body-Environment Co-Design
- Brain-Body Co-Design Benchmarks
- Brain-Body Co-Design in Real World
- Other Surveys Recommended
- ✉ Contact Information
- Star History
Detailed information can be found in our survey paper.
| Paper | Published in | Co-Designed Agent | Code&Page |
|---|---|---|---|
| Subtract to adapt: Autotomic robots | RoboSoft 2023 | Modular Soft Robot | N/A |
| A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies | SSCI 2023 | Modular Rigid Robot | N/A |
| How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots | ALIFE 2024 | Modular Robot | N/A |
| Co-Optimization of Robot Design and Control: Enhancing Performance and Understanding Design Complexity | Arxiv 2024 | Mixed Robots | Code |
| Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots | EuroGP 2024 | Modular Soft Robot | N/A |
| Paper | Published in | Co-Designed Agent | Code&Page |
|---|---|---|---|
| Curriculum-based co-design of morphology and control of voxel-based soft robots | ICLR 2023 | Modular Soft Robot | Code&Page |
| PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training | CoRL 2023 | Modular Soft Robot | Code&Page |
| Learning to Design and Use Tools for Robotic Manipulation | CoRL 2023 | Robot Manipulator Tools | Page |
| Paper | Published in | Co-Designed Agent | Code&Page |
|---|---|---|---|
| Co-Designing Manipulation Systems Using Task-Relevant Constraints | ICRA 2022 | Rigid Manipulator | N/A |
| LLM-POET: Evolving Complex Environments using Large Language Models | GECCO 2024 Companion | 2D Modular Soft Robot (Voxel-Based Soft Robot) |
N/A |
| Evolving Complex Environments in Evolution Gym using Large Language Models | ICASSPW 2024 | 2D Modular Soft Robot (Voxel-Based Soft Robot) |
N/A |
| Paper | Published in | Co-Designed Agent | Code&Page |
|---|---|---|---|
| Shape change and control of pressure-based soft agents | ALIFE 2022 | pressure-based soft robot | Code&Page |
| DittoGym: Learning to Control Soft Shape-Shifting Robots | ICLR 2024 | Soft Shape-Shifting Robots | Code&Page |
| Paper | Published in | Co-Designed Agent |
|---|---|---|
| Soft Robots Learn to Crawl: Jointly Optimizing Design and Control with Sim-to-Real Transfer | RSS 2022 | Soft Robot |
| DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models | NIPS 2023 | Soft Robot |
| Computational synthesis of locomotive soft robots by topology optimization | Science Advances 2024 | Soft Robot |
| Co-Designing Manipulation Systems Using Task-Relevant Constraints | ICRA 2022 | Rigid Manipulator |
| A ’MAP’ to find high-performing soft robot designs: Traversing complex design spaces using MAP-elites and Topology Optimization | IROS 2024 | Rigid Manipulator |
| Using neuroevolution for designing soft medical devices | BIR 2024 | Soft Medical Device |
| Structural Optimization of Lightweight Bipedal Robot via SERL | IROS 2024 | Bipedal Robot |
| Evolution and learning in differentiable robots | RSS 2024 | Rigid Robot |
| Co-Designing Tools and Control Policies for Robust Manipulation | Arxiv 2024 | Robot Tools |
- Exploring Embodied Intelligence in Soft Robotics: A Review
Zikai Zhao, et al., 2024, Bio-Inspired and Biomimetic Intelligence in Robotics - Collective Intelligence for Deep Learning: A Survey of Recent Developments
David Ha and Yujin Tang, 2022, Collective Intelligence - Bridging evolutionary algorithms and reinforcement learning: A comprehensive survey on hybrid algorithms
Pengyi Li and Jianye Hao, et al., 2022, IEEE Transactions on Evolutionary Computation - Design Optimization of Soft Robots: A Review of the State of the Art
FeiFei Chen and Michael Yu Wang, 2022, IEEE Robotics & Automation Magazine
This repo is developed and maintained by Yuxing Wang.
For any questions, please feel free to email [email protected].