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.
PhD Defense Celebrations in the Lab
Congratulations to the HCRL students for their PhD’s
- Optimization Approaches for High-Performance and Efficient Utilization of Redundancy in Robotic Systems by Jeeeun Lee
- Towards Human Centered Deployment of Autonomously Exploring Robots by Ryan Gupta
- Advancing Frontiers of Path Integral Theory for Stochastic Optimal Control by Apurva Patil
HCRL Spin-Off Apptronik Secures $350M to Revolutionize Humanoid Robotics
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.
Final Class Projects for Learning for Dynamics and Controls Class
Panel on Responsible AI Innovation
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.”
Celebrating Seung Hyeon Bang’s PhD
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:
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
Excited to Share our Latest Collaborative Work with Dexterity on Heavy Object Manipulation using Multi-Suction-Cup Grippers
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.