In this section
@inproceedings{Cully:2019:10.1145/3321707.3321804,
author = {Cully, A},
doi = {10.1145/3321707.3321804},
pages = {81--89},
publisher = {ACM},
title = {Autonomous skill discovery with quality-diversity and unsupervised descriptors},
url = {http://dx.doi.org/10.1145/3321707.3321804},
year = {2019}
}
TY - CPAPER
AB - Quality-Diversity optimization is a new family of optimization al-gorithms that, instead of searching for a single optimal solutionto solving a task, searches for a large collection of solutions thatall solve the task in a different way. This approach is particularly promising for learning behavioral repertoires in robotics, as sucha diversity of behaviors enables robots to be more versatile and resilient. However, these algorithms require the user to manually define behavioral descriptors, which is used to determine whethertwo solutions are different or similar. The choice of a behavioral de-scriptor is crucial, as it completely changes the solution types thatthe algorithm derives. In this paper, we introduce a new method to automatically define this descriptor by combining Quality-Diversityalgorithms with unsupervised dimensionality reduction algorithms. This approach enables robots to autonomously discover the rangeof their capabilities while interacting with their environment. The results from two experimental scenarios demonstrate that robot canautonomously discover a large range of possible behaviors, without any prior knowledge about their morphology and environment. Furthermore, these behaviors are deemed to be similar to hand-crafted solutions that uses domain knowledge and signicantly more diverse than when using existing unsupervised methods.
AU - Cully,A
DO - 10.1145/3321707.3321804
EP - 89
PB - ACM
PY - 2019///
SP - 81
TI - Autonomous skill discovery with quality-diversity and unsupervised descriptors
UR - http://dx.doi.org/10.1145/3321707.3321804
UR - http://hdl.handle.net/10044/1/69961
ER -
