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A PhD is usually where you learn what research _is_ and how it is done, that's the reason why it is usually a 'minimum requirement' If you plan to spend some y

A PhD is usually where you learn what research _is_ and how it is done, that's the reason why it is usually a 'minimum requirement' If you plan to spend some years in reading and implementing papers to become a researcher anyway.. You can probably go back to school and get a PhD, this will be way more efficient than randomly reading without guidance. Furthermore, a researcher do not spend his days to implement state of the art algorithms, he tries to push science a little further in very small steps, implementing is just part of the process. Even when you have a PhD, pure research jobs are pretty rare but from your description you are more looking into innovative startups that implement SoTA papers (more R&D than research), this is more common and most of the jobs in robotic require some experimental setup and "research". In those startup the PhD requirement is often flexible ! But this "research" is often very different from the research done in academy. Try to clarify what kind of research you want to do so that people can give you more precise advices :)

NalNezumi 6y ago
Thanks for the reply and the valuable feedback! >If you plan to spend some years in reading and implementing papers to become a researcher anyway.. You can probably go back to school and get a PhD, this will be way more efficient than randomly reading without guidance. Well the core question is just that: if my goal is to become a researcher, should I just go for a PhD, or is there another way. And also the reason I resigned from my work, to first see how far I can improve my understanding on my own (and how much that can amount to finding a research opportunity). >Furthermore, a researcher do not spend his days to implement state of the art algorithms, he tries to push science a little further in very small steps, implementing is just part of the process. I'm aware that implementing SoTA is not necessarily research, but still often something done by PhD while PhD to understand/test/reproduce/get insight of a certain SoTA approach, to build/modify on it. >Even when you have a PhD, pure research jobs are pretty rare I'm also aware of this fact, as I met plenty of PhD people at my previous start up(mostly doing the implementation I mentioned above, in their respective field of expertise), although most of them wasn't that interested in research anymore. I use research to cover both the process of discovering new and noble things, and finding a suitable SoTA for a specific engineering problem (usually what PhD going to industry end up doing, it seems) because as I see it, it is also valuable "research" in the field of robotics. (For context, my particular interest is in Reinforcement Learning application in Robotics) Engineering application of theory in industries seems like a good "feedback" to the academic community too =) But I'm aware that my view of things is limited/naive, and that's why I'm asking for others experience. Maybe I should've been a bit more specific, though!
- somethingsome 6y ago
  RL is extensively used in robotics ;) If you want to go for a PhD, I suggest reading Sutton's book on RL (at least the important parts for what you want to do), followed by recent RL papers and RL papers in robotics that pick your interest, understand them fully. Aim for journal papers and major conferences only (high impact factor is a first metric that comes to mind). The main bit is to try to formulate an important research question you're interested in which is not already explored in the vast litterature and plan which experiments you need to carry and what do you need to change if some of them fail. If you want to engage in a PhD, an important and well studied research question will increase your chances of getting accepted in a PhD program. The main advantage in doing a PhD in a uni is that you can share and learn knowledge from other students and advisors, as well as have insights in what are ideas worth pursuing and what are small improvement that no one care about. At uni you usually work on the kind of research meant to improve the knowledge and less on the general engineering problems. Engineering problems are byproduct of the knowledge you are building. A lot of uni work very closely with industries in practical fields like robotics, you'll have plenty of opportunities to apply what you are building directly on engineering problems. But that will not be the main focus. In my experience it is considered less 'valuable research' to try to apply some SoTA to a specific engineering problem in academy, and usually proposals of this kind are rejected. Hope this is helpful :)
  - NalNezumi 6y ago
    >I suggest reading Sutton's book on RL I've read part of the book, and followed David Silvers UCL lectures on my own, and have been focused on reading all the recent algorithm/approaches in the field last few weeks. >RL is extensively used in robotics ;) Is it? From Andrej Karpathy:s HN comment[1] and Alex Ipran's[2] comment in 2017 & 2018 I feel like it is just recently (around mid 2019) actual trial on robotics have started to show promise (SAC, E2E) in academia, and only a handful of start ups have approached this (Covariant, founded by one of the professor in the two papers above) But then again, I might be missing some insight here. Thank you for the extremely valuable feedback! :) The formulation of important research question is also something I'm trying to figure out by, getting myself updated in the field to the point of understanding what is missing (or under-studied)! As I mentioned in another comment I'm in the process of applying at a public research institute (as an research assistant) with potential for an industrial PhD position, so those feedback is just on-point valuable for me right now :) (Although the research is not RL focused but is trying to tackle the identical problem formulation in robotics using ML/AI algorithms) [1] https://news.ycombinator.com/item?id=13519044 https://news.ycombinator.com/item?id=13519044 [2] https://www.alexirpan.com/2018/02/14/rl-hard.html https://www.alexirpan.com/2018/02/14/rl-hard.html

NalNezumi 6y ago

Thanks for the reply and the valuable feedback! >If you plan to spend some years in reading and implementing papers to become a researcher anyway.. You can probably go back to school and get a PhD, this will be way more efficient than randomly reading without guidance. Well the core question is just that: if my goal is to become a researcher, should I just go for a PhD, or is there another way. And also the reason I resigned from my work, to first see how far I can improve my understanding on my own (and how much that can amount to finding a research opportunity). >Furthermore, a researcher do not spend his days to implement state of the art algorithms, he tries to push science a little further in very small steps, implementing is just part of the process. I'm aware that implementing SoTA is not necessarily research, but still often something done by PhD while PhD to understand/test/reproduce/get insight of a certain SoTA approach, to build/modify on it. >Even when you have a PhD, pure research jobs are pretty rare I'm also aware of this fact, as I met plenty of PhD people at my previous start up(mostly doing the implementation I mentioned above, in their respective field of expertise), although most of them wasn't that interested in research anymore. I use research to cover both the process of discovering new and noble things, and finding a suitable SoTA for a specific engineering problem (usually what PhD going to industry end up doing, it seems) because as I see it, it is also valuable "research" in the field of robotics. (For context, my particular interest is in Reinforcement Learning application in Robotics) Engineering application of theory in industries seems like a good "feedback" to the academic community too =) But I'm aware that my view of things is limited/naive, and that's why I'm asking for others experience. Maybe I should've been a bit more specific, though!

somethingsome 6y ago
RL is extensively used in robotics ;) If you want to go for a PhD, I suggest reading Sutton's book on RL (at least the important parts for what you want to do), followed by recent RL papers and RL papers in robotics that pick your interest, understand them fully. Aim for journal papers and major conferences only (high impact factor is a first metric that comes to mind). The main bit is to try to formulate an important research question you're interested in which is not already explored in the vast litterature and plan which experiments you need to carry and what do you need to change if some of them fail. If you want to engage in a PhD, an important and well studied research question will increase your chances of getting accepted in a PhD program. The main advantage in doing a PhD in a uni is that you can share and learn knowledge from other students and advisors, as well as have insights in what are ideas worth pursuing and what are small improvement that no one care about. At uni you usually work on the kind of research meant to improve the knowledge and less on the general engineering problems. Engineering problems are byproduct of the knowledge you are building. A lot of uni work very closely with industries in practical fields like robotics, you'll have plenty of opportunities to apply what you are building directly on engineering problems. But that will not be the main focus. In my experience it is considered less 'valuable research' to try to apply some SoTA to a specific engineering problem in academy, and usually proposals of this kind are rejected. Hope this is helpful :)
- NalNezumi 6y ago
  >I suggest reading Sutton's book on RL I've read part of the book, and followed David Silvers UCL lectures on my own, and have been focused on reading all the recent algorithm/approaches in the field last few weeks. >RL is extensively used in robotics ;) Is it? From Andrej Karpathy:s HN comment[1] and Alex Ipran's[2] comment in 2017 & 2018 I feel like it is just recently (around mid 2019) actual trial on robotics have started to show promise (SAC, E2E) in academia, and only a handful of start ups have approached this (Covariant, founded by one of the professor in the two papers above) But then again, I might be missing some insight here. Thank you for the extremely valuable feedback! :) The formulation of important research question is also something I'm trying to figure out by, getting myself updated in the field to the point of understanding what is missing (or under-studied)! As I mentioned in another comment I'm in the process of applying at a public research institute (as an research assistant) with potential for an industrial PhD position, so those feedback is just on-point valuable for me right now :) (Although the research is not RL focused but is trying to tackle the identical problem formulation in robotics using ML/AI algorithms) [1] https://news.ycombinator.com/item?id=13519044 https://news.ycombinator.com/item?id=13519044 [2] https://www.alexirpan.com/2018/02/14/rl-hard.html https://www.alexirpan.com/2018/02/14/rl-hard.html