Ravi Hammond

I’m an aspiring Artificial Intelligence researcher specialising Robotics and Reinforcement Learning. Currently, I am a MSc student in Computer Science in the Australian Institute for Machine Learningat the University of Adelaide, supervised by Prof. Ian Reid. I’ve spent time interning as a Software Engineer at Google, Microsoft, Palantir Technoligies, and Atlassian.

news

Sep, 2021	Our team (University of Adelaide) placed 3rd in the NASA Space Robotics Challenge Phase 2, and also received the innovation award for our solution. Here is a demo.
Jul, 2021	Enrolled into a Computer Science Master of Philosophy at the University of Adelaide.
Feb, 2021	Designated Project Lead of TeamUniAdelaide for the NASA Space Robotics Challenge.
Nov, 2020	Started as a Software Engineering Intern at Atlassian, Sydney.
Dec, 2019	Started as a Software Engineering Intern at Google, Sydney.
Dec, 2019	Graduated with a Bachelor of Computer Science from the University of Adelaide.
Nov, 2018	Awarded the Adelaide Summer Research Scholarship.
Jan, 2017	Started as a Software Engineering Intern at Microsoft, Redmond.

selected publications

arXiv

Robotic Vision for Space Mining

Sachdeva, Ragav, Hammond, Ravi, Bockman, James, Arthur, Alec, Smart, Brandon, Craggs, Dustin, Dzung, Anh-Dzung, Rowntree, Thomas, Schutz, Elijah, Orenstein, Adrian, Yu, Andy, Chin, Tat-Jun, and Reid, Ian

arXiv preprint arXiv:2109.12109 2021

Abs arXiv PDF Blog

Future Moon bases will likely be constructed using resources mined from the surface of the Moon. The difficulty of maintaining a human workforce on the Moon and communications lag with Earth means that mining will need to be conducted using collaborative robots with a high degree of autonomy. In this paper, we explore the utility of robotic vision towards addressing several major challenges in autonomous mining in the lunar environment: lack of satellite positioning systems, navigation in hazardous terrain, and delicate robot interactions. Specifically, we describe and report the results of robotic vision algorithms that we developed for Phase 2 of the NASA Space Robotics Challenge, which was framed in the context of autonomous collaborative robots for mining on the Moon. The competition provided a simulated lunar environment that exhibits the complexities alluded to above. We show how machine learning-enabled vision could help alleviate the challenges posed by the lunar environment. A robust multi-robot coordinator was also developed to achieve long-term operation and effective collaboration between robots.