Towards Long-term Autonomy: A Perspective from Robot Learning

Zhi Yan, Li Sun, Tomas Krajnik, Tom Duckett, and Nicola Bellotto
Proceedings of AAAI-23 Bridge Program on AI & Robotics

Abstract

In the future, service robots are expected to be able to operate autonomously for long periods of time without human inter-vention. Many work striving for this goal have been emerging with the development of robotics, both hardware and soft-ware. Today we believe that an important underpinning of long-term robot autonomy is the ability of robots to learn on site and on-the-fly, especially when they are deployed in changing environments or need to traverse different environ-ments. In this paper, we examine the problem of long-term autonomy from the perspective of robot learning, especially in an online way, and discuss in tandem its premise “data” and the subsequent “deployment”.

@misc{yan2023longterm,
      title={Towards Long-term Autonomy: A Perspective from Robot Learning}, 
      author={Zhi Yan and Li Sun and Tomas Krajnik and Tom Duckett and Nicola Bellotto},
      year={2023},
      eprint={2212.12798},
      archivePrefix={arXiv},
      primaryClass={cs.RO}
}