The Human Centered Robotics Group

Kyle had a great time presented his workshop paper on “Learning Contact-based Navigation in Crowds” at the 2nd Workshop on Human-Interactive Robot Learning (HIRL 2023) at the ACM/IEEE International Conference on Human-Robot Interaction.

LINK TO PAPER

 

Friends and colleagues. The IEEE-RAS Humanoids 2023 website is up and running! It will be held in Austin, USA on December 12-14. Note that the paper deadline is July 1st. We look forward to sharing this experience with academics and industry within the field of humanoids and human-centered robots. Additional details on the particular topics for the conference will be posted soon.

Click on the image to be directed to the Humanoids 2023 Website.

Apptronik, a spin-off of our lab is featured in IEEE Spectrum focusing on robots to perform dull, dirty, and dangerous tasks. Congratulations Nick Paine and Appttronik team! Click on the image below for a link to the article.

 

 

 

Our lab got three papers accepted for the IEEE International Conference on Robotics and Automation in London 2023. Congratulations to students Mingyo Seo, Jeeeun Lee, Ryan Gupta, Jaemin Lee, and collaborators! Click on the images below to access pre-prints of the papers.

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Our new NSF Growing Convergent Research project focusing on Community Embedded Robotics had several spotlights on the news this month, e.g.

Click here for a link to USA Today’s news coverage

See below for video coverage from KVUE:

We can’t believe that we finished teaching the second year of ASE 389 Learning for Dynamics and Controls. Here is the syllabus of the class:

LINK TO SYLLABUS:
shorturl.at/bctHK

We had great project presentations on the prediction of dynamical systems and the control of mechanisms using ML techniques. Check the video below:

 

Dr. Sentis gave a talk at the 2nd Workshop on perceptive locomotion.

https://sites.google.com/view/perceptive-locomotion/

Can robots walk fast in narrow corridors with moving people and objects? In collaboration with Prof. Yuke Zhu we tackle this problem via a hierarchical learning framework dubbed, PRELUDE.

Link to the project’s website: Click here
Link to a pre-print of the paper: Click here
Link to the code: Click here

Here we share our recent research on performing multi-robot search in outdoor and indoor environments.

Link to paper
Link to project website