I am looking to recruit several PhD students in human-robot interaction, accessibility, haptics, and virtual reality for fall 2024. If you are interested, please complete this form: forms.gle/qkBBqaJsAAiLky… ASU School of Computing and Augmented Intelligence ASU Ira A. Fulton Schools of Engineering Arizona State University

Congrats to #SCAI Associate Professor Siddharth Srivastava who has been named a Scialog Fellow by Research Corporation for Science Advancement. As one of only approximately 50 new Fellows, Srivastava will work to develop innovative approaches and solutions to multidisciplinary problems. rescorp.org/scialog/aut-fe…

The program for the #AAAI 2024 Spring Symposium on User-Aligned Assessment of Adaptive AI Systems is now available. We have a fantastic lineup of invited speakers. The symposium starts this Monday. Link: bit.ly/aia24-program Sriraam Natarajan Siddharth Srivastava Hazem Torfah AAAI

It’s a wrap! Check out this recap of the excellent #AAAI 2024 Spring Symposium, held last week at Stanford, facilitated by #SCAI Associate Professor Siddharth Srivastava and his #PhD student Pulkit Verma. Thanks to all who attended and shared their work and perspectives.

Congratulations to Dr. Pulkit Verma for completing his dissertation with a fabulous presentation and defense today! 🍾🎇🥳 Here's a big thanks to the committee members: Nancy Cooke, Georgios Fainekos, and Yu Zhang for all their time and effort along the way!

Congratulations to Dr. Naman Shah for a successful PhD defense and a great dissertation talk! 🥳🍾🎉 A special thanks to the committee, George Konidaris, Subbarao Kambhampati (కంభంపాటి సుబ్బారావు), Alberto Speranzon and Yu Zhang!

I got hooded recently. It was great to have my parents with me, who have been incredibly patient and supportive throughout my PhD journey. And, of course, thanks to Siddharth Srivastava, without whose amazing mentorship, this would not have been possible. ASU School of Computing and Augmented Intelligence #ASUgrad #PhDone

Congratulations to Pulkit Verma!! His dissertation helped kickstart the emerging area of user-driven assessment of AI systems that can learn and plan.

I will also present our work on epistemic exploration in non-stationary settings, which shows that it is worth learning a STRIPS-like model if you want better efficiency and lower costs. Joint work w/ Rushang Karia, Alberto Speranzon, Siddharth Srivastava Link: ojs.aaai.org/index.php/ICAP…

I'll present our work "Epistemic Exploration for Generalizable Planning and Learning in Non-Stationary Settings" at #ICAPS2024 today. Joint Work w/ Rushang Karia, Alberto Speranzon, Siddharth Srivastava ⏰ 01:45 PM MDT in Room KC 203 ICAPS Conference Join the session to learn more.

Excited to announce 8th Workshop on Generalization in Planning (#GenPlan2025) at AAAI (#AAAI2025)! Consider submitting your work and join us as we explore generalization and transfer in sequential decision-making with a stellar line-up of speakers. aair-lab.github.io/genplan25/

Researchers at Arizona State University are using AI to teach robots how to do jobs that humans don't want. The new technology would make the deployment of robots substantially less expensive and less time-consuming: ow.ly/u7kN50TePpf

Curious to hear about creating generalist robots from leaders in the field? Don’t miss our panel “Representations for Generalist Robots” (4-5pm) Conference on Robot Learning LEAP workshop! Feat. Chelsea Finn Animesh Garg Vincent Vanhoucke Marc Toussaint Siddharth Srivastava and Leslie Kaelbling!

And it’s a wrap to an excellent workshop. Thanks to all the invited speakers, panelists, contributors, and co-organizers. #LEAP #CoRL24

Excited to share the success of the #IJCAI2025 Workshop on User-Aligned Assessment of Adaptive AI Systems. It featured invited talks from Reid Simmons, Xujie Si, and Ruqi Zhang; and a panel discussion with Reuth Mirsky, Sriraam Natarajan, Reid, and Xujie Si. 🌐bit.ly/ijcai25-aia

Robots can finally invent their own symbols for reasoning and planning 🤯. We show the first method that learns symbolic world models directly from raw demos—no labels, no hand-coded actions. This lets robots generalize to tasks 18× larger than training. 👇🧵