Move over Savanna 🐘 and Forest 🐘! Our #ICLR2023 paper brings in a brand new 🐘 to Rwanda : The RLHF 🐘 👇

📢 Although LLMs suck at the search needed to generate executable plans, they do have some approx. planning knowledge . Can we tease this out as world/domain models, and use them to drive planning? (w/ Lin Guan, Karthik Valmeekam & Sarath Sreedharan) 1/ 👉 arxiv.org/abs/2305.14909

📢📢 Our #ICAPS2023 tutorial "On the role of Large Language Models in Planning"--all 3 hours of it--presented at ICAPS Conference in Praha this Monday, is now online. 👉 youtube.com/playlist?list=… If you want to know whether or not LLMs can plan, this is just the ticket.. 1/

📣Chalk it to mad masochism, but we will be updating and reprising our tutorial on LLMs and Planning at #AAAI2024 Vancouver, BC. (Wed Feb 21st, 2-6PM 👉aaai.org/aaai-conferenc…. We are told that ~300 folks signed up already..😱). w/ Karthik Valmeekam Lin Guan

Robots now understand decisions via SERLfD – Self-Explanation for Reinforcement Learning from Demonstrations, transcending traditional robot learning from human demos. Check out our article and slides; find more insights at poster 409 during AAAI-24 on 2/23, 7-9 PM!🤖🌐 #AAAI2024

📢📢 Come stop by #AAAI2024 poster spots 644 & 645 (the very last spots) this evening to hear me explain the neat work of 👉Zahra Zahedi & Sailik Sengupta on explaining allocations to humans 👉Yantian Zha & Lin Guan on using self explanations to learn from ambiguous demonstrations

📢 Yochanite Lin Guan (Lin Guan; guansuns.github.io), currently a Research Scientist with Meta GenAI, will be defending his ASU School of Computing and Augmented Intelligence PhD dissertation tomorrow 10/21 👇. He developed several 🔥 techniques for taming the notorious sample complexity of #RL systems..