A big congratulations to HCRL’s student Seung Hyeon Bang for graduating with a PhD in Aerospace Engineering titled “Reactive and Predictive Whole-body Control for Agile, Robust, Versatile, and Deployable Humanoids”. 
Archives for November 2024
Design and Assembly of LEGATO Handheld Learning Gripper
We are sharing, open source, our new design and assembly for our LEGATO Handheld Gripper for cross-embodiment robot learning. Instructions for design and assembly are here below:
https://ut-hcrl.github.io/LEGATO-Gripper
New Hardware-Accelerated Ray-Tracing Method for Enhanced Volume Mesh Collision Detection
In collaboration with Dexterity Inc., we introduce a unique hardware-accelerated ray-tracing method for direct volume mesh-to-mesh discrete collision detection, which particularly excels in continuous collision detection. Kudos to Andrew Bylard, who guided this work following his groundbreaking research, and to my student Sizhe Sui, who led the technical implementation.
Link to Paper:
Cross-Embodiment Handheld Gripper Study
Introducing our latest collaborative work, named LEGATO, which describes a universal handheld smart gripper and a motion-invariant policy, that enable efficient cross-embodiment skill transfer between human users and robots with different morphologies, enhancing scalability and versatility in robotics. Congratulations to my PhD student Mingyo Seo who has led the study. In addition, many thanks to The AI Institute, Andy Park and Yuke Zhu (co-advisor of Mingyo) for their incredible contributions and support. Many thanks to Mitch Pryor for providing access to his Spot robot. Many thanks to the Office of Naval Research for supporting this project.
Pointer to paper:
https://arxiv.org/abs/2411.03682
Project Website (includes code and various videos):
https://lnkd.in/gBeW8pQZ