UPDATED: Due to inclement weather, GCSD will be closed on Monday, Dec. 2. District offices open. Stay safe and warm! ❄️

Rt to put AB in this game

Gilmore and Jackson have given up one touchdown all season, and it was a lateral to a quarterback on a trick play.

Sasuke 😈🔥

Congratulations to graduating senior Sheridan Dillon who will be attending Fairfield University to play baseball! Sheridan is planning on majoring in Business! Good luck Sheridan!

Congratulations to graduating senior Sean O'Brien who will be attending University of Buffalo to run cross country and track! Sean is planning on majoring in Engineering! Good luck Sean!

How can we enable LLMs to actively clarify ambiguous task specifications by gathering information from humans? Check out APRICOT at #CoRL2024! APRICOT combines LLMs, which propose diverse questions, with Bayesian Active Learning, which selects the most informative one to ask.

🚀 Come checkout the *four* papers at #CoRL2024 from Cornell PoRTaL Group ! Each explores a unique angle on how robots can learn effectively with humans: - Learning by asking (APRICOT) - Learning by watching (RHyME, Time Your Rewards) - Learning to collaborate (MOSAIC) Details: 🧵👇

We would love to train our robots with human videos. But humans move very differently from robots! How do we bridge this divide? Check out our work at #ICRA2025 “One-Shot Imitation under Mismatched Execution” on specifying tasks to robots via a prompt human video 🧵

We’re incredibly proud to see our work featured by the Cornell Chronicle! This work was co-led with the amazing Prithwish Dan and advised by Sanjiban Choudhury Read the full story here - news.cornell.edu/stories/2025/0…

A core challenge we face when training robots from human videos (e.g., MotionTrack) is that the human basically has to move like a robot for it to work... Kushal and Prithwish Dan have gone deep on this over the past year—first of many exciting papers that will solve this!

Say ahoy to 𝚂𝙰𝙸𝙻𝙾𝚁⛵: a new paradigm of *learning to search* from demonstrations, enabling test-time reasoning about how to recover from mistakes w/o any additional human feedback! 𝚂𝙰𝙸𝙻𝙾𝚁 ⛵ out-performs Diffusion Policies trained via behavioral cloning on 5-10x data!