Also not excited for the first occurrence of this on OpenReview
14.02.2026 10:34
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Also not excited for the first occurrence of this on OpenReview
Prime example (found on the other platform) of why we should be careful with reward specification / alignment / guardrails / <enter your favorite AI safety topic here>.
How much of this is human guided, and how much is just optimizing the “get PR merged” reward?
github.com/matplotlib/m...
Take a look at our new paper!
We improve sample efficiency and performance in off-policy RL by prioritizing experience with the semantic knowledge of a pre-trained VLM, and not even a very large one 🌍🤖📈🏆
Glad for the opportunity to work with @eladsharony.bsky.social and @tomjur.bsky.social !
Are there any good robotics and/or RL podcasts still running in 2025?
I used to enjoy The Robot Brains by Pieter Abbeel and TalkRL by @robinchauhan.bsky.social , but open to different styles too!
I find this idea really neat - VLMs are great at describing scenes, but LLMs are better reasoners, so let's use text as an interim representation.
Kind of reminiscent of the bitter lesson, only on a more "local" scale
arxiv.org/abs/2503.15108
Check out our new #ICLR2025 paper: EC-Diffuser leverages a novel Transformer-based diffusion denoiser to learn goal-conditioned multi-object manipulation policy from pixels!👇
Paper: www.arxiv.org/abs/2412.18907
Project page: sites.google.com/view/ec-diff...
Code: github.com/carl-qi/EC-D...
Also probably an issue of salience bias - you hear about virtually every plane crash and a lot of shootings, but road fatalities rarely make the news.
If interested on our take on addressing inverse RL in large state spaces, go to meet @filippo_lazzati and @alberto_metelli in the poster session 5 #NeurIPS2024 today (paper -> arxiv.org/abs/2406.03812)
That speaks to the lack of good, standardized benchmarks for RL, more than anything else.
(Disclaimer: haven’t read the papers yet)
I agree completely. I just think the challenge will remain policy and public perception regarding public transit, same as it is today - just amplified by the effort that's been put into the technology by car manufacturers.
This is actually something that worries me - how can we ensure that all the progress in autonomous driving doesn't just put a lot more single-person cars on the road? And also, how do we convince people that this isn't an alternative, and that we need to keep investing in transit.
Want to learn / teach RL?
Check out new book draft:
Reinforcement Learning - Foundations
sites.google.com/view/rlfound...
W/ Shie Mannor & Yishay Mansour
This is a rigorous first course in RL, based on our teaching at TAU CS and Technion ECE.
Been thinking about building a replacement for the arXiv daily email for a while, this looks like it might save me the trouble :)
Just out of curiosity: what’s the action space here?
Let’s use the real data to improve the simulators and get better massive, procedurally generated data 🤩
Some papers really feel like a glimpse into the future!
This one also serves as a powerful reminder that a lot of what we're focused on in the AI + robotics space is constrained by the hardware we have.
arxiv.org/abs/2411.11192
