Robot Metabolism: Towards machines that can grow by consuming other machines

Philippe Martin Wyder, Riyaan Bakhda, Meiqi Zhao, Quinn A. Booth, Matthew E. Modi +15 more
11/17/2024
cs.ROcs.MAeess.SY

No confident code implementation found for this paper.

Abstract

Biological lifeforms can heal, grow, adapt, and reproduce -- abilities essential for sustained survival and development. In contrast, robots today are primarily monolithic machines with limited ability to self-repair, physically develop, or incorporate material from their environments. While robot minds rapidly evolve new behaviors through AI, their bodies remain closed systems, unable to systematically integrate material to grow or heal. We argue that open-ended physical adaptation is only possible when robots are designed using a small repertoire of simple modules. This allows machines to mechanically adapt by consuming parts from other machines or their surroundings and shed broken components. We demonstrate this principle on a truss modular robot platform. We show how robots can grow bigger, faster, and more capable by consuming materials from their environment and other robots. We suggest that machine metabolic processes like those demonstrated here will be an essential part of any sustained future robot ecology.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{wyder2024robot,
  title  = {Robot Metabolism: Towards machines that can grow by consuming other machines},
  author = {Philippe Martin Wyder and Riyaan Bakhda and Meiqi Zhao and Quinn A. Booth and Matthew E. Modi and Andrew Song and Simon Kang and Jiahao Wu and Priya Patel and Robert T. Kasumi and David Yi and Nihar Niraj Garg and Pranav Jhunjhunwala and Siddharth Bhutoria and Evan H. Tong and Yuhang Hu and Judah Goldfeder and Omer Mustel and Donghan Kim and Hod Lipson},
  year   = {2024},
  eprint = {2411.11192},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.11192v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2411.11192.svg)](https://codeofpaper.com/paper/2411.11192)

Discussion