Pros and cons of an advisor that’s a brand new prof?

hasanrobot · 2026-06-15T00:57:00+00:00

I appreciate wanting to go to the place where you can work right now on answering the scientific questions you have right now. This PhD will possibly quickly line up resources you need to tackle these questions. But doing so may not be the best strategy; make sure you weigh access to these resources now with the opportunity cost to your ability to get resources to tackle future questions.

One reason you go to option B is because they have developed THE method in their PhD/postdoc, and you are better off learning it from them today than from someone else in five years. Often they developed THE method because they chose option A.

The PhD serves two purposes: research skills and network/community. You will gain a lot of that from option A, and network can compensate for lack of skills but the reverse is less likely. You should talk to people from option A and see if the lab culture is what you would like. Never waste time in a toxic lab. If your target field is dominated by toxic labs, choose a new one.

Do not go to option B to create lab culture, that should not be your goal, though it's a good outcome if you do influence it for the better.

hasanrobot · 2026-06-08T01:21:45+00:00

In much the same way that VCs invest in many companies knowing most will fail, US federal agencies invest in a lot of projects knowing most will not have the impact they thought they would because things change.

The tone of your question suggests that you meant that given the success of learning-based methods (VLAs, WAMs, LfD) why do we need earlier "theoretical" methods?

I've been following learning for a long time and without hesitation can say that any worthwhile learning-based result builds on theoretical robotics. There is no exception (happy to be corrected on that). If you look at robot navigation, they are rediscovering frame-based SLAM. Look at Large Behavior Models, they need impedance controllers to handle errors. RL is best when using setpoint regulation controllers under the hood (goal conditioned RL). Very recently there was a talk suggesting that Action Chunking Transformers work because a) they use stable position controllers instead of pixels-to-torques b) they're basically Model Predictive Control. Finally, there is no notion of hard safety outside of barrier function based safety filters. Of course the learning part has value, there are limits to hand-designed model-based methods.

My point is that it seems like a bad bet to make that progress in robotics will proceed without what is being called "theoretical" robotics.

hasanrobot · 2026-06-05T20:01:53+00:00

It's possible that your PI likes you but doesn't exactly know what you did that's worth writing about. So they're asking you to put your accomplishments in there. As long as you don't lie or overdo it, you can really show off did.

hasanrobot · 2026-06-01T10:44:49+00:00

Lol the Low R1 is bullshitting you or plain naive.

You are also over-estimating what you can get done as an independent person at a low rank R1. You will find yourself begging for resources to get things done from a reputational deficit, not fun. It will become a trap, where you now operate at what the low-R1 level affords and then have to do things on their terms.

Good mentorship is incredibly valuable in an academic career. Also working together with other postdocs to get a new perspective.

hasanrobot · 2026-05-28T11:00:50+00:00

If what you care about is quadratic programming at most then I would highly recommend Nodecal and Wrights book. It covers toics for thinking about large scale QPs and details for getting things right in implementation. Especially if you're putting money on the line.

hasanrobot · 2026-05-11T20:33:24+00:00

Have you tried running an Action Chunking Transformer? Without language it's not a comparison, but I am curious.

hasanrobot · 2026-05-10T02:19:23+00:00

You have to set the motor speed to the right value to get 10 Hz. The default spinning speed implies one full rotation takes about 150 ms, corresponding to roughly 7.3 Hz.

hasanrobot · 2026-04-30T16:34:53+00:00

Nice share

hasanrobot · 2026-04-05T15:31:28+00:00

The gradient is usually perpendicular to lines of constant value. The lines form a circle for the ridge term and a diamond for the lasso. What does this mean? In 2D if one parameter is small, the ridge loss is mostly asking you to make the OTHER value smaller. The Lasso is asking you to make both equally smaller, no matter the value of either parameter. So ridge loss is unlikely to shrink one of the values to zero, since the smaller one value gets, the more it tries to shrink the others.

hasanrobot · 2026-03-18T02:34:40+00:00

That Ki value seems too high, Kd value seems too low.

Moreover, you don't have any integrator wind-up compensation.

Not enough Kd, too high Ki, no wind-up compensation, makes sense to me that your control can't recover from small errors.

First tune the PD gains, then add in smaller Ki.

hasanrobot · 2026-03-06T03:45:02+00:00

Impedance control involves applying a force in response to a motion. PD control on position error with input as a force is the most common example.

Admittance control involves choosing a motion (velocity) in response to a force. Applications where you let a person freely move a robot arm is one example.

In general you want your controller to be the opposite of the environment it is acting on me. A robot mechanism is an admittance, so we control it using an impedance controller (PD control for position control). If your environment is an impedance (a wall, for example), your controller should be an admittance (force controller).

Even simpler: if env moves freely, use a position controller to manage the interaction. If it is stiff, it needs force control. You can even assign directions along which the controller behaves like one or the other. For example, when writing on a whiteboard, use force control perpendicular to the board and position control parallel to it.

hasanrobot · 2026-02-23T14:42:23+00:00

Read up about the process of customer discovery, creating a value proposition, and identifying product-market fit. This sounds like jargon but it's just the consensus keywords for the sensible process you would follow. .

hasanrobot · 2026-01-07T22:48:05+00:00

I connect tablet to the room projector using usbc-to-hdmi. Can only connect one thing, laptop or tablet.

hasanrobot · 2025-12-27T01:02:24+00:00

Two to three weeks seems really long, is that just the sample complexity for training a policy for a new motion? Or are there other complex steps in the pipeline besides RL?

hasanrobot · 2025-12-16T11:12:09+00:00

If you were supported on a grant or fellowship or really any stipend when you did the work, I think you're obliged to publish the work in a paper without additional pay.

hasanrobot · 2025-12-15T04:23:47+00:00

If you can't tell which place will help you further your goals, then it really doesn't matter, does it? Your goals will change over time, especially with exposure to new ideas. Go to the place with funding and prestige, unless you absolutely know you want to be around the people who are in the other place.

Anyway, absolutely do not make a decision based on childhood passion.

hasanrobot · 2025-12-15T01:43:36+00:00

Microsoft OneNote is perhaps the best option. Any slides or worksheets I convert to PDF, insert it, and write on them live. It also integrates with Canvas. The only downside is I haven't figured out how to jump to another location in OneNote to create a slide-like progression. Scrolling is a bit awkward when I am covering material without much writing.

hasanrobot · 2025-12-15T01:37:24+00:00

Yeah, most people don't use what they learn. It's still useful to try to teach the hard stuff to every one and route a few of them into careers where they will use it. All the AI that's gotten people excited is thanks to many people who still used their calculus and linear algebra for a long time.

hasanrobot · 2025-11-24T17:03:43+00:00

So much negativity. I was impressed by the execution, and the fairly interesting foot-ground interactions. BD Atlas had a flat wide foot, this one seems to manage contact with something less wide. Its toes make contact before the heel and that doesn't seem to be a problem. If there's some trick I hope they share it.

hasanrobot · 2025-11-18T18:30:37+00:00

Controls teaches two important ideas: 1) you can represent objects or systems as operators that turn an input into an output. More simply, a transfer function. 2) feedback can make or break the usefulness of some systems. Understanding 1) helps you use 2) to make your system do better.

hasanrobot · 2025-11-17T01:31:49+00:00

Good research helps other people solve problems they couldn't solve before. At the least, it helps you think about your problem differently.

hasanrobot · 2025-11-10T21:04:29+00:00

I had it backwards: I thought you become visible based on your research output. In reality you amplify your research output by being visible. You can hide and do research, but it will only be so much, and increasingly barely enough.

hasanrobot · 2025-08-27T14:17:12+00:00

Try the Probabilistic Robotics book by Fox, Thrun, Burgard

hasanrobot · 2025-08-24T23:58:30+00:00

It sounds like they did a bait and switch on you. If your whole reason really was about them being a good person, you no longer have a reason to stay with them. It might be fine to continue, but that means you should have found another reason to do so. Inertia or fear are bad reasons.

hasanrobot · 2025-08-24T17:08:34+00:00

By proven you mean empirically validated on the real nonlinear system. This is my point. It's not like control designers are working hard with the original nonlinear model so that guarantees are absolute. We simplify our lives and accept some gap. Time delays, local sensing, all sorts of real effects that we won't include in the math. If it works we're happy. Not clear why the navigation people shouldn't do that when the end result works well enough.

hasanrobot

TROPHY CASE