TL;DR: 2019 was a mixed year and a local minimum (as I would like to
believe). In the first half of the year (January through May), my focus
was solely on aesthetics - physical fitness and art. This came at the
cost of progress in my research and my intellectual development. By the
start of June, I had begun to realize the effects of this skewed focus
and I took drastic corrective actions. In the second half of the year
(June through November), I focused solely on getting my research up to
speed by curtailing my physical activities. Though this led to limited
progress in my research (as opposed to zero progress in the first half),
it entailed heavy stress that is not sustainable in the long term.
First Half (Jan - May)
I took to three habits:
I had started going to the gym towards the end of 2018 and had made
limited gains. I wanted to make visible changes in my body and for
this I trained hard - choosing not too frequent (thrice a week) but
intense and long (2 hrs) workout sessions. Unfortunately, my recovery
wasn't quick enough and I would feel tired after these intense workouts.
This negatively affected my work. It also negatively affected my sleep
quality (or was it coffee?) which further degraded my work-life balance.
However, I had decided to go all in and hence chose to ignore these
warning signs as I felt that physical fitness and non-negotiable aspect
of my life.
In the end, I did notice visible changes in my physique. This
was the fittest I had ever been - 8 pull-ups, 75 kg squats, 50 kg
bench-press and 5.5 miles in 52 mins.
However, this intensity was unsustainable.
I also spent some time learning how to play the piano - I took piano
lessons and even bought a keyboard. However, I did not feel like
continuing it because it was yet another solitary hobby in the list
of solitary activities that I do. I found a fun alternative - Latin
dancing, in the summer.
Second Half (June - Nov)
By this time, I had made zero progress in my research despite my
attempts to maintain a work-life balance (basically aesthetics/work
balance). My advisor wanted me to submit a paper by the end of the year,
which was simply not possible if I did not take corrective actions.
So I stopped working out, reading books and piano and tried to spend
more time working. This led to an interesting realization- I was not
really interested in what I was working on and further that I did not
really understand the value of my research. I do not like working on
short-term objectives. I have always liked to go after the bigger
picture. Not being able to understand where my research was headed was a
major peeve and rather demoralizing.
I was aiming for ICRA (due in September) but missed the deadline. I then
aimed for ICAPS in November. Even though my paper had very weak results,
I still submitted the paper to keep my sanity. I do not like missing
important deadlines (the practical reason being that it is a slippery
Publishing papers regularly is non-negotiable during a Ph.D; not just because it keeps you in good standing in the program but also because it keeps you on your toes.
Do not worry about the quality of your paper during the initial days. Just submit!
Always ensure that you are aware of the bigger picture where your research fits. This requires studying consistently (read books) and keep abreast of the latest research (read papers).
Bouncing Back (Dec)
As I finished my course projects, exams towards the end of November, I could feel that I was gradually having a better sense of the research being done around me and even my own research. Surprisingly, this was a sum-total of a number of unplanned and providential experiences/interactions, for example- my course projects that I ignored for the most part, one-off discussions with colleagues and coming across eye-opening papers.
This reinforces my belief that for good research, it is critical to stay around the center of gravity. In the case of robotics and AI, CMU is that place.
Next, every once in a while, it is important to read books on fundamental ideas. You would anyway read books on the latest algorithms and techniques. It pays to study problems and formulations that are more abstract. For example, study classical RL vs the latest DRL papers, study MDP's vs RL, study Dynamic Programming vs MDP's.
In my case, this realization came as I studied Dynamic Programming and Optimal Control by Dmitri Bertsekas and 'A Unified Framework for Sequential Decisions'_. Both these readings (the latter being a chance discovery) studied together emphasize how well connected the fields of Heuristic Search, Optimal Control, RL and MDP are - they can all be looked at through the same Dynamic Programming lens.
For the first time, I now have a good idea who my ancestors are (ideologically speaking). Whence I could only look up to Dijkstra and Judea Pearl, I now have all the stars of optimal control and DP to look up to - Bellman, Bertsekas and so on.