This paper reviews the large spectrum of methods for generating robot motion proposed over the 50 years of robotics research culminating in recent developments. It crosses the boundaries of methodologies, typically not surveyed together, from those that operate over explicit models to those that learn implicit ones. The paper discusses the current state-of-the-art as well as properties of varying methodologies, highlighting opportunities for integration.
@article{arxiv.2410.12172,
title = {The State of Robot Motion Generation},
author = {Kostas E. Bekris and Joe Doerr and Patrick Meng and Sumanth Tangirala},
journal= {arXiv preprint arXiv:2410.12172},
year = {2024}
}
Comments
Presented at the International Symposium of Robotics Research (ISRR), 2024. Website: https://pracsys.cs.rutgers.edu/papers/the-state-of-robot-motion-generation/