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Re: undefined

#31

Post by blovescoffee »

NalNezumi wrote: post #19

This seem like an cool upgrade from RT1, judging from the result. It seems to now also output the delta of the end-effector pose, which was previously handled by a different motion planner. It does seem like this work (and a lot of robot learning works) are still stuck on position/velocity control and not impedance control. Which is essentially output where to go, either closed-loop with a controller or open-loop wit…

They collect data for RL/IL in simulation which _can_ generalize to the real world. Also, being Google, they have the resources to collect data by brute force i.e. scientists manually collecting that data. The paper says - one main source of the data is internet scale vision/llm data. The second source is 6k trial runs.

A principal idea behind this work is that you can collect data in one domain to avoid collecting data in another. Training an LLM can train the 'reasoning' portion of the robot so that it can perform real-world skills with less training.

Re: undefined

#32

Post by RecycledEle »

RecycledEle wrote: post #30
empath-nirvana wrote: post #26

Earlier quoted context omitted.

Seems to me that the way to architect this is to have multiple quasi independent embedded controllers at different levels. For example, you might have 3 independent finger controllers, managed by a hand controller, and so on, on up to the highest level LLM that drives everything. So you have an LLM that just says, pick up the green can, issues whatever structured data needs to go to the arm controller and on down the…

I agree. We need feedback loops on each joint that keep doing what they are doing until a higher feedback loop decides to change things. For example, as I enter this text, most of my fingers are holding the phone and do not need to be told to keep holding it. The low level controllers should also be able to respond to simple things. It's like when I get hurt, my hand yanks back before my brain realizes what has happe…

LLMs have terrible spatial awareness. This probably comes from ONLY being trained on text.

I wonder if it would help a LLM running a robot to have a separate controller calculating it's position and what is around it, and feed that into the LLM constantly.

It would be like when a video game has a radar display or map to let you know where you are in relation to other things.

Re: undefined

#33

Post by NalNezumi »

empath-nirvana wrote: post #26
NalNezumi wrote: post #19

This seem like an cool upgrade from RT1, judging from the result. It seems to now also output the delta of the end-effector pose, which was previously handled by a different motion planner. It does seem like this work (and a lot of robot learning works) are still stuck on position/velocity control and not impedance control. Which is essentially output where to go, either closed-loop with a controller or open-loop wit…

Seems to me that the way to architect this is to have multiple quasi independent embedded controllers at different levels. For example, you might have 3 independent finger controllers, managed by a hand controller, and so on, on up to the highest level LLM that drives everything. So you have an LLM that just says, pick up the green can, issues whatever structured data needs to go to the arm controller and on down the…

That is indeed what a lot of Machine Learning turned Robotics researchers/enthusiasts are banking on. A counter argument to that is what Dhruv Batra responded to the question "lol why not use LLM for everything" [1].

>As a human, I don't understand the detailed micro-second by microsecond movements my fingers have to do to pick something up, let alone how I'm touch-typing this sentence. It just sort of "happens" when I want it to happen. I don't think you need to design a robotic AI that understands how every part of it's mechanics work.

This is true for us ofc, and is encompassed in what is called the "Moravec's paradox" [2]. You don't understand it because it's unconscious process, and It's harder to reverse-engineer an unconscious process (motor movement) than conscious ones (calculating math, playing game, writing text, reading).

But the thing is that in the real world we do need to take in to account everything, including noise and time-delay. Evolution gave rise to complex language in the last 100k years compared to millions of year for motor movement. I do agree that there must be some "hierarchical" structure for complex motion, but we currently don't know where and how that hierarchy is. Boston dynamics uses Model Predictive Control for complex movement, which means that at least some model of the world is required, for dexterous motion. Now if that model is part of LLM or not is a hard guess.

But if we don't know this it's hard to say what kind of data we need to collect to train a LLM-model applied to embodiment (robotics).

Researchers in the past have already made the mistaken assumption of "oh cognition and language is the hard part of intelligence. Perception and motion is easy" [3] and then their work amounted to nothing because turns out the latter was way, wayyyyyy harder.

There's an implicit bias in us that think Language, puzzles and logic are harder [2] and therefore models that accomplish this can just be rammed in to the "easier" issues.

Edit: I too would like "LLM models will solve these" attitude because otherwise the research I'm doing right now is a dead-end, but the more I try (with my limited compute) the less I'm sure

[1] https://imgur.com/eWsH5ui originally https://twitter.com/DhruvBatraDB/status/1641871357020614656

[2] https://en.wikipedia.org/wiki/Moravec%27s_paradox

[3] https://youtu.be/x10964w00zk?list=PLSQhB89mdG7PsZsDz2_5hZL8C...

Re: undefined

#34

Post by NalNezumi »

blovescoffee wrote: post #31
NalNezumi wrote: post #19

This seem like an cool upgrade from RT1, judging from the result. It seems to now also output the delta of the end-effector pose, which was previously handled by a different motion planner. It does seem like this work (and a lot of robot learning works) are still stuck on position/velocity control and not impedance control. Which is essentially output where to go, either closed-loop with a controller or open-loop wit…

They collect data for RL/IL in simulation which _can_ generalize to the real world. Also, being Google, they have the resources to collect data by brute force i.e. scientists manually collecting that data. The paper says - one main source of the data is internet scale vision/llm data. The second source is 6k trial runs. A principal idea behind this work is that you can collect data in one domain to avoid collecting d…

They use the RT1 data which is (if I remember correctly) 11 month of data using like 6 research engineers sitting everyday, remote controlling the robot. They might even use ROSIE data which is data-augmentation using Stable-diffusion inpainting.

And for that they can do mobile manipulation, which have been possible for a long while (as long as we know ground-truth of object location, and have a some-what consistent map. Although the latter is solved with SLAM). it is way, way more versatile (if their claims are correct) so this is cool result.

as for sim2real, that's a whole different set of issue.... Simulator have come a long way, but from last time I talked with team-lead of Mujoco sim, it seems like completely realistic ones (at a reasonable compute time) is still far away. Deform-able objects are hard to simulate

My point is just that the "reasoning" you mention have a limit;[1] there's no amount of reasoning with language that can zero-shot a robot to do gymnastics or ride a bide. I'd be more than happy to be proven wrong on this point though, then my line of research is not dead-end =)

[1] https://youtu.be/x10964w00zk

Re: undefined

#35

Post by thanatropism »

aliljet wrote: post #4

Robotics is such a lovely world. For the hobbyists, I'm really curious about how to get the home-brew robotics training lab working at home? The last time I reviewd this, you'd spend thousands of dollars just to get a reasonable robotic arm. Right now, if work is relegated to the rich research departments of mega corporations, this certainly doesn't seem more interesting than a corporate press release..

Is there any reason why anthropomorphic is needed for an enjoyable hobby? Get an Arduino, a couple of servos, make a claw that grips on things.

Re: undefined

#37

Post by JimtheCoder »

falcor84 wrote: post #27
Jeff_Brown wrote: post #14

If it was tethered to the floor, I would trust a good LLM in 2023 with folding laundry, but very little else. No dishes. No letting kids or pets bear it. No plumbing. I wouldn't even let it wander around cleaning stuff, because I'd expect it to knock over things.

I personally am less concerned about knocking things over, which is a thing we already trust vacuum robots with; rather I'd be much more concerned about the robots accidentally (or intentionally?) "folding" my kids or pets.

I would like to see one try to fold my cat.

I would then have one cat and one messed up LLM robot.

Re: undefined

#38

Post by martythemaniak »

I'm actually pretty bullish on humanoid robots like the Tesla bot - combination of LLMs, cheap batteries/motors/controllers from cars and vision research should be able to come together in useful and cheap ways in a few years, say 2030.

$35K for a robot that can putter around the house doing basic stuff is just not that high of a bar. With a 10 year life span, that's $3.5k/year, or $10/day. Doing 1 hour of useful minimum wage work around the house is just not that high of a bar - doing laundry, cleaning, tidying up, weeding, wiping surfaces down, taking out the garbage etc. If it can do some combination of those, it would make sense for basically every household. And it doesn't need to be able to do the crazy parkour of Boston Dynamics to achieve this. Our world is generally designed to be operable by all sorts of people - disabled, old etc. Crazy athleticism isn't required to do useful work.

Re: undefined

#39

Post by jimbokun »

empath-nirvana wrote: post #26
NalNezumi wrote: post #19

This seem like an cool upgrade from RT1, judging from the result. It seems to now also output the delta of the end-effector pose, which was previously handled by a different motion planner. It does seem like this work (and a lot of robot learning works) are still stuck on position/velocity control and not impedance control. Which is essentially output where to go, either closed-loop with a controller or open-loop wit…

Seems to me that the way to architect this is to have multiple quasi independent embedded controllers at different levels. For example, you might have 3 independent finger controllers, managed by a hand controller, and so on, on up to the highest level LLM that drives everything. So you have an LLM that just says, pick up the green can, issues whatever structured data needs to go to the arm controller and on down the…

Maybe the octopus is a more tractable model for robotic control. I understand that octopus neurons are not as concentrated in a central brain, but spread throughout its limbs that are autonomous compared to humans.

Re: undefined

#40

Post by HereBePandas »

Jeff_Brown wrote: post #14

If it was tethered to the floor, I would trust a good LLM in 2023 with folding laundry, but very little else. No dishes. No letting kids or pets bear it. No plumbing. I wouldn't even let it wander around cleaning stuff, because I'd expect it to knock over things.

Sure, but this time in 2022, you probably wouldn't have let it do that much (or even had this thought).

Progress!

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