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Redesigning and remanufacturing robots are infeasible for resource-constrained environments like space or undersea. This work thus studies how to evaluate and repurpose existing, complementary, quadruped legs for new tasks. We implement this approach on 15 robot designs generated from combining six pre-selected leg designs. The performance maps for force-based locomotion tasks like pulling, pushing, and carrying objects are constructed via a learned policy that works across all designs and adapts to the limits of each. Performance predictions agree well with real-world validation results. The robot can locomote at 0.5 body lengths per second while exerting a force that is almost 60% of its weight.
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EvoRobogami: co-designing with humans in evolutionary robotics experiments
We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally.
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- Award ID(s):
- 1845339
- PAR ID:
- 10399576
- Date Published:
- Journal Name:
- GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference
- Page Range / eLocation ID:
- 168 to 176
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3512290.3528867
