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Congratulations to Junhyeok Ahn for successfully defending his PhD dissertation. You can access the written dissertation by clicking here.

Congratulations to Jee-Eun for her new paper accepted in Frontiers in Robotics and AI, titled: “Safe Robot Climbing in Unknown Structures”. The work is a collaboration with Tirtha Bandyopadhyay from Australia’s CSIRO Data61 group. While the paper is in press, you can view the video below:

We had a wonderful semester teaching ASE 389 Decision and Control of Human-Centered Robots. You can find the syllabus below:

Link to Class Syllabus

In addition, students pursued projects in human-centered robotics focusing mostly on trajectory generation and control. Here is a video presentation of one of the projects:

Ph.D. student Jaemin Lee defended his dissertation which addresses problems at the intersection of trajectory generation, optimization, and control of humanoid robots. You can find a link to his Ph.D. thesis here:

 

 

Our paper “Mixtures of Controlled Gaussian Processes for Dynamical Modeling of Deformable Objects” has been accepted for the Learning for Dynamics & Control Conference, 2022. Congratulations to Ce Xu and our collaborators at the Polytechnic University of Catalonia (UPC)!

 

Here is a video of the content of the class and selected project presentations:

Ye Zhao, Yinan Li, Luis Sentis, Ufuk Topcu, Jun Liu, Reactive Task and Motion Planning for Robust Whole-Body Dynamic Locomotion in Constrained Environments, The International Journal of Robotics Research, In Press 2022

This study takes a first step toward formally and reactively task planning and whole-body dynamic loco-manipulation behaviors in constrained and dynamically changing environments. We formulate a two-player temporal logic game between the multi-limb locomotion planner and its dynamic environment to synthesize a winning strategy that delivers symbolic locomotion actions. A controller further executes low-level motion primitives that generate feasible locomotion trajectories.

In this video, three ground robots — two A1 quadrupeds and one HSR mobile platform — sweep an environment quickly in search of a missing volleyball ball. They achieve this capability by employing online path planning and heterogeneous clustering to accommodate for the different speeds and fields of view of each robot. The algorithm supports up to 50 robot/vehicle search and is robust to robot/vehicle failures by re-planning at runtime. This technology is geared towards tasks such as emergency response where people could be missing during a fire or flood.

 

This work describes an online gain adaptation method to enhance the robustness of whole-body controllers for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC can easily become unstable, and therefore the desired task goals may not be achievable. The proposed method serves as a low-level controller for tracking whole-body trajectories more robustly than using fixed gain feedback control methods. Link to abstract:

https://www.frontiersin.org/articles/10.3389/frobt.2021.788902/abstract

 

Our lab is announcing multiple NSF Ph.D. Fellowships in Ethical AI starting 2022 through 2025! Good Systems, a UT Grand Challenge, and Texas Robotics are recruiting our first cohort of NSF Research Traineeship Ph.D. Fellows in Ethical AI. Prospective students interested in working in the HCRL can apply for doctoral admission in our department. More information can be found under the Fellowship tab: http://shorturl.at/gAEY9.