As part of the XPrize Wildfire competition, Prof. Zwerneman poses with UTASE students and the wildfire fighting aircraft designs they have developed in coordination with the HCRL.

Congratulations to the HCRL students for their PhD’s

1. Optimization Approaches for High-Performance and Efficient Utilization of Redundancy in Robotic Systems by Jeeeun Lee
2. Towards Human Centered Deployment of Autonomously Exploring Robots by Ryan Gupta
3. Advancing Frontiers of Path Integral Theory for Stochastic Optimal Control by Apurva Patil

Apptronik, a spin-off from the HCRL, co-founded by former student Nick Paine and PI Luis Sentis, secures $350M to advance Humanoid Robots. A great example of how “What Starts Here, Changes the World!” Click here or on on the image to read the full news article.

Students explore various projects in the areas of path integral control, learning for prosthetics, RL for vision games, RL for underwater vehicles, RL for grasping, and imitation learning for robotics among several highly engaging research topics.

Moderator Luis Sentis (Chair of Good Systems, UT Austin) engaged with panelists Chelsea Collier (Digi.City), Matt Lease (UT Austin School of Information), Daniel Culotta (City of Austin), Junfeng Jiao (UT Austin School of Architecture), and the AILive team, to deliver an engaging and insightful discussion on “Responsible AI Innovation.”

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”.

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

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:

https://arxiv.org/abs/2409.09918v1

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

This comprehensive paper introduces a model for improving the grasp strength of multi-suction-cup grippers, addressing challenges in heavy object manipulation. It presents new constraints for trajectory planning and optimization, solves load distribution issues with a quadratic program, and validates the model through experimental results.