These days, we see robots built out of almost anything, and learning to move in all kinds of crazy ways. Basically, developing “gaits” for a mobile machine is becoming a computation that can be done on demand.
Researchers from Tokyo report on a dramatic case of this: ad hoc robots built with tree branches, that learn to ‘walk’ [2]. The robot is constructed from some generic connectors that have motors and sensors. These parts are attached to branches, to create weird ‘robots’.
The gait is developed by 3D scanning the natural branches, and then using machine learning to learn how to walk in a 3D simulation. The results are odd, but effective. “It isn’t how well the sticks walk, it’s that they walk at all.”
This is basically the same technology that is used for developing conventional robots though you don’t have to scan the structural parts if you make them to order. The machine learning is very general, and this project demonstrates that it can work with parts that are very different from anything humans would deliberately design.
The upshot seems to be that it is possible to construct a functioning crawler out of “found objects”. Artists have long been excited by the possibilities of repurposing objects, and this technique allows an artist to make a robot from whatever they choose.
Evan Ackerman points out that this sort of robot might be practically useful in certain situations [1]. For one thing, it could allow creating special or one off robots out of generic hardware and local materials.
I wonder if you could also make temporary field repairs, replacing a broken leg with an ad hoc stump form a tree branch, and then learn to limp.
I imagine that this concept could be extended to other aspects of robotic function. I think that you could construct an ad hoc manipulator out of tree branches, as well as structures such as cargo baskets and sensor pods.
It would be interesting to see how well this concept could be scaled down. Imagine a swarm of little bots, built out of twigs and grass!
- Evan Ackerman, Robots Made Out of Branches Use Deep Learning to Walk, in IEEE Spectrum – Robotics. 2019. https://spectrum.ieee.org/automaton/robotics/robotics-hardware/robots-tree-branches-deep-learning-walk
- Azumi Maekawa, Ayaka Kume, Hironori Yoshida, Jun Hatori, Jason Naradowsky, and Shunta Saitu, Improvised Robotic Design With Found Objects, in Workshop on Machine Learning for Creativity and Design at NeurIPS 2018. 2018: Montreal. https://nips2018creativity.github.io/doc/improvised_robotic_design.pdf
Robot Wednesday
Robert McGrath's Blog 