We can answer questions! But also, I want to highlight that the "AlphaNIST" name is an homage to the very well known work from David Silver's research group @ Deepmind circa 2016->2018. This was when we were coming up very early in our research careers and their work obviously had a big impact on us!
It has taken longer to make some of those ideas work for areas as challenging as robotics where there are physical constraints to deal with (and admittedly in this paper, we do not show the transfer to real hardware quite yet). But, we have some glimmer of evidence it is looking possible here (in another recent paper) that we cranked on for the last 6 months and will be featured at CoRL: https://x.com/octi_zhang/status/2108583315544682914
We are also all mega-lurkers here so we were honored to be featured on the front page!
The movements of the robot gripper arm and its interaction with the longer objects ("Single-component assembly experiment", 1:00 and onwards) somehow really reminds me of how a horse moves it head.
Really cool stuff. I especially liked the flicking behavior. Very organic movements.
In case somebody got the same question as me: the robot "sees" point clouds as input, so basically the full simulation state except for velocities (correct me if I'm wrong). As put in the limitations section, it "[does] not establish robustness to perception error, latency, or contact-model mismatch".
I’m highly entertained by the way the robot tightens nuts. There is no way this would work in the real world :)
(This whole thing is awesome. I think they will need to get the number of rollouts needed down before it translates well to real robots — running hundreds of thousands of rollouts on small parts with a real rollout will be a real logistical mess when cleaning up parts on the floor and dealing with everything that the robots physically break while learning.)
The starting states being at different points in the task and having success-guided starting state sampling seem like really neat ideas. I haven't kept up with RL in a while, how novel is this?
Hi, one of the authors here!
We can answer questions! But also, I want to highlight that the "AlphaNIST" name is an homage to the very well known work from David Silver's research group @ Deepmind circa 2016->2018. This was when we were coming up very early in our research careers and their work obviously had a big impact on us!
It has taken longer to make some of those ideas work for areas as challenging as robotics where there are physical constraints to deal with (and admittedly in this paper, we do not show the transfer to real hardware quite yet). But, we have some glimmer of evidence it is looking possible here (in another recent paper) that we cranked on for the last 6 months and will be featured at CoRL: https://x.com/octi_zhang/status/2108583315544682914
We are also all mega-lurkers here so we were honored to be featured on the front page!
ICYMI, the "alpha" is a reference to "alphabet", i.e., Google, after deepmind was acquired. You have branded your paper as coming from Google.
This is a robotics paper by Nvidia & UW researchers.
It is not an Alphabet/Deepmind paper, and it also has nothing to do with the "NIST" OCR dataset.
Was there really not a more appropriate name than "AlphaNIST"?
Woah, deepmind solved the national institute of standards and technology???
It is a NIST board challenge. https://www.nist.gov/el/intelligent-systems-division-73500/r...
NIST makes sense, but co-opting Alpha is a bit odd.
Alpha historically had meant "Google DeepMind" not RL
The movements of the robot gripper arm and its interaction with the longer objects ("Single-component assembly experiment", 1:00 and onwards) somehow really reminds me of how a horse moves it head.
Really cool stuff. I especially liked the flicking behavior. Very organic movements.
In case somebody got the same question as me: the robot "sees" point clouds as input, so basically the full simulation state except for velocities (correct me if I'm wrong). As put in the limitations section, it "[does] not establish robustness to perception error, latency, or contact-model mismatch".
I’m highly entertained by the way the robot tightens nuts. There is no way this would work in the real world :)
(This whole thing is awesome. I think they will need to get the number of rollouts needed down before it translates well to real robots — running hundreds of thousands of rollouts on small parts with a real rollout will be a real logistical mess when cleaning up parts on the floor and dealing with everything that the robots physically break while learning.)
Without clicking the link. What is it? How about a short description?
Alphanist - Some app/lib/code that does something
It's the companion blog post to an Nvidia / UW paper of an approach to solving learning based high precision robotics tasks.
HN guidelines are to use the title of the blog post and not editorialize.
HN guidelines don’t preclude the submitter from including a descriptive first comment on the story.
Fair - I should have
The starting states being at different points in the task and having success-guided starting state sampling seem like really neat ideas. I haven't kept up with RL in a while, how novel is this?