If you were at the last RLC NeurIPS event, you know it's not to be missed. Invite any RL researcher you know, no invite-only parties here.
10.12.2024 21:56
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If you were at the last RLC NeurIPS event, you know it's not to be missed. Invite any RL researcher you know, no invite-only parties here.
Robotics manipulation to be specific :)
Can someone point me to any paper that uses RL on real-life (image-based) environments without sim2real/imitation learning? For good reasons I am told that this is pretty common but I’ve only found a handful of papers (CQN, QT-Opt, SAC-X)
Hi Csaba :)
Streaming Deep Reinforcement Learning Finally Works, by
M. Elsayed, G. Vasan, A. R. Mahmood, is one of those papers I wish I had written 😅
This paper seems to allow us to do RL with NNs as it should have always been done. Everyone should read it!
arxiv.org/abs/2410.14606
I also think many robot learning papers are overclaiming what they can do… the paper task setup can be very easy in comparison to real production systems even for pick-n-place… but it’s hard to see this difference in the presented videos (if any)
I feel like many works do have experiments on sim but they don’t seem to transfer to real life (not in terms of sim2real but applying the same algo). I wonder how much of it comes from delays or us being overprotective of the robot in real life. Maybe evals need to include these components.
I thought that was just me 😅 was trying it on an uncluttered single item picking task
Perhaps it’s “necessary” to have it as a baseline (arguably TD3 is just as important imo but SAC seems to be more commonly used), and it’s hard to convince people which one is stronger than SAC. I think recently there are few e.g. ACE at Neurips. Generally feels like a popularity game to me
Let me try, we’ve been very quiet historically 😂
Can I get added please :)
Please see me :)
Please add me, I’ve been doing robot learning with RL/IL!