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ahmed-hendawy.bsky.social

@ahmed-hendawy.bsky.social
14 followers 27 following 27 posts

ahmedhendawy.de

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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 27/07/2026
The RL4VLA Workshop has come to an end after an incredible Friday at #RSS2026 in Sydney 🇦🇺 We brought together researchers to discuss how RL can advance VLA models, the remaining challenges, and what it will take for robotics to have its own "RL moment" as we've seen with LLMs.
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 15/06/2026
🚨 Less than 24 hours to submit your work to our RL4VLA workshop @rss #RSS2026
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 09/06/2026
🚨 Deadline Extension Alert! The RL4VLA Workshop submission deadline has been extended by one week! 🗓️ New deadline: June 15 (AoE) Still time to submit your work. See you at #RSS2026
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 02/06/2026
Enjoying #ICRA2026? 🤖 Submit to the 𝗥𝗟𝟰𝗩𝗟𝗔 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 @ #RSS2026 🇦🇺 📅 Deadline: 𝗝𝘂𝗻𝗲 𝟴 (𝗔𝗼𝗘) 📍 Sydney 🗓️ 𝗝𝘂𝗹𝘆 𝟭𝟳 RL × VLA × Robotics 🤖 OpenReview: openreview.net/group?id=rob... Reviewers welcome: 🔗 docs.google.com/forms/d/e/1F... #RSS2026 #RL #Robotics #VLA
openreview.net
RSS 2026 Workshop RL4VLA
Welcome to the OpenReview homepage for RSS 2026 Workshop RL4VLA
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 13/05/2026
🚀 Excited to announce the 𝗥𝗟𝟰𝗩𝗟𝗔 Workshop @ #RSS2026 in Sydney, Australia 🇦🇺 Bringing together researchers working on reinforcement learning for Vision Language Action (VLA) systems, embodied AI, and scalable robot learning. 📅 Submission deadline: June 8th, 2026 (AoE)
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 22/04/2026
I'm in Rio 🇧🇷 for #ICLR! Catch me presenting MINTO 🌿 this Friday, April 24 | Afternoon | Pavilion 4 #4603. MINTO 🌿 uses the online network when it helps speed up RL training without sacrificing stability. Let's chat about RL and your research. DM me or simply come say hi!
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ahmed-hendawy.bsky.social @ahmed-hendawy.bsky.social · 11/02/2026
🧵 Accepted at @iclr-conf.bsky.social! Target networks stabilize bootstrapping in RL 🛡️ But induce slow-moving targets 🐢 Online networks adapt fast ⚡ But can diverge with function approximation 💥 𝗠𝗜𝗡𝗧𝗢 🌿 uses the online network 𝗼𝗻𝗹𝘆 𝗶𝗳 𝗶𝘁 𝗰𝗮𝗻 — yielding faster 𝘢𝘯𝘥 more stable RL. Here’s how 👇
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