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Thanks for sharing [1], that was a great read. I'd be curious to see an updated version of that article, since it's about 6 years old now. For example, Boston D
by ubj 2y ago
Thanks for sharing [1], that was a great read. I'd be curious to see an updated version of that article, since it's about 6 years old now. For example, Boston Dynamics has transitioned from MPC to RL for controlling its Spot robots [2]. Davide Scaramuzza, whose team created autonomous FPV drones that beat expert human pilots, has also discussed how his team had to transition from MPC to RL [3].
[2]: https://bostondynamics.com/blog/starting-on-the-right-foot-with-reinforcement-learning/ https://bostondynamics.com/blog/starting-on-the-right-foot-w...
[3]: https://www.incontrolpodcast.com/1632769/13775734-ep15-davide-scaramuzza-vision-based-navigation-agile-drone-racing-perception-aware-control-and-event-cameras https://www.incontrolpodcast.com/1632769/13775734-ep15-david...
- alessiodm 2y agoThank you for the amazing links as well! You are right that the article [1] is 6 years old now, and indeed the field has evolved. But the algorithms and techniques I share in the GitHub repo are the "classic" ones (dating back then too), for which that post is still relevant - at least from an historical perspective. You bring up a very good point though: more recent advancements and assessments should be linked and/or mentioned in the repo (e.g., in the resources and/or an appendix). I will try to do that sometime.