Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

update future talks #93

Merged
merged 1 commit into from
Nov 12, 2024
Merged

update future talks #93

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 14 additions & 0 deletions website/talks/future_talks/index.mdx
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,20 @@ In this talk, we introduce multi-AI agents built on top of AutoGen to improve ef

### Sign Up: https://discord.gg/8WFKcVN2?event=1300487847681196082

## Integrating Foundation Models and Symbolic Computing for Next-Generation Robot Planning - Nov 18, 2024

### Speakers: Yongchao Chen

### Biography of the speakers:

Yongchao Chen is a PhD student of Electrical Engineering at Harvard SEAS and MIT LIDS. He is currently working on Robot Planning with Foundation Models under the guidance of Prof. Chuchu Fan and Prof. Nicholas Roy at MIT and co-advised by Prof. Na Li at Harvard. He is also doing the research in AI for Physics and Materials, particularly interested in applying Robotics/Foundation Models into AI4Science. Yongchao interned at Microsoft Research in 2024 summer and has been working with MIT-IBM Watson AI Lab starting from 2023 Spring.

### Abstract:

State-of-the-art language models, like GPT-4o and O1, continue to face challenges in solving tasks with intricate constraints involving logic, geometry, iteration, and optimization. While it's common to query LLMs to generate a plan purely through text output, we stress the importance of integrating symbolic computing to enhance general planning capabilities. By combining LLMs with symbolic planners and solvers, or guiding LLMs to generate code for planning, we enable them to address complex decision-making tasks for both real and virtual robots. This approach extends to various applications, including task and motion planning for drones and manipulators, travel itinerary planning, website agent design, and more.

### Sign Up: https://discord.gg/Swn3DmBV?event=1303162642298306681

## How to follow up with the latest talks?

Join our community Discord (https://discord.gg/sUkGceyd) to be the first to know about amazing upcoming talks!
Expand Down
Loading