We are honored to receive the best paper award published in 2018 from IEEE Transactions on Mechatronics (TMech). Huge congratulations to Donghyun Kim, Junhyeok Ahn, Nick Paine, and Orion Campbell for their effort and contribution to the paper. Click the image below to access the paper:
Virtual presentation and paper on coordinating simultaneous manipulation and locomotion for humanoids and dexterous legged robots.
S. Jorgensen, M. Vedantam, R. Gupta, H. Cappel, and L. Sentis, Finding Locomanipulation Plans Quickly in the Locomotion Constrained Manifold, IEEE International Conference on Robotics and Automation, 2020
https://arxiv.org/pdf/1909.08804.pdf
During his thesis, Steven has develop theory and implementation that ultimately allowed the humanoid robot Valkyrie to be deployed in a real world setup. Great accomplishment!
Our paper, lead by former student Ye Zhao, PI Luis Sentis, and collaborators, Yinan Li, Ufuk Topcu and Jun Liu, Reactive Task and Motion Planning for Robust Whole-Body Dynamic Locomotion in Constrained Environments, is conditionally accepted to The International Journal of Robotics Research. It describes a symbolic planning and subsymbolic control architecture for humanoid robots to autonomously navigate in complex dynamic environments.
Our paper, lead by students Donghyun Kim, Steven Jorgensen, Jaemin Lee, Junhyeok Ahn, Jianwen Luo, and PI Luis Sentis, Dynamic Locomotion For Passive-Ankle Biped Robots And Humanoids Using Whole-Body Locomotion Control, is now accepted to The International Journal of Robotics Research. Congratulations to the team! It allows line foot robots such as Mercury and Draco to dynamically walk and recover from push disturbances using Whole-Body Controllers.
We are happy to share that Miguel Arduengo, a joint student between Catalonia’s UPC and our lab at UT Austin, was awarded the best undergraduate thesis prize in artificial intelligence by the Catalan Association of AI. Click on the image for the pdf.
PI, Dr. Sentis, gave this talk for a 400 people general audience of all ages focusing on engaging K-12 students in robotics.
Introducing our work on control of liquid cooled viscoelastic bipedal robots. Apptronik developed for UT Austin this excellent humanoid lower body robot, dubbed DRACO, and students at UT’s HCRL lab devised actuator control algorithms and integrated Time-to-Velocity Reversal Locomotion and Whole-Body Locomotion Control algorithms. The result is unsupported dynamic balancing of DRACO. A link to the paper preprint is here (click on the image):
HCRL students, alumni and friends having a good time after presenting and giving a talk at ICRA 2019.
Our lab just returned from ICRA 2019 where we had a terrific time. We’re sharing a video of the talk at the workshop on legged robots: https://icra2019wslocomotion.wordpress.com/program
Apptronik and the Human-Centered Robotics Lab at UT Austin have joined forces to develop a force augmentative exoskeleton called Sagittarius. The video shows a subject wearing a 12 degree-of-freedom human-interactive and high-power density lower-body exoskeleton developed by Apptronik. A whole-body augmentative exoskeleton control algorithm has been jointly developed, allowing the exoskeleton to remove gravitational payload while standing or walking.
B. He, H. Huang, G.C. Thomas, L. Sentis, Complex Stiffness Model of Physical Human-Robot Interaction: Implications for Control of Performance Augmentation Exoskeletons, Submitted, 2019
R. Schlossman, M. Kim, U. Topcu, L. Sentis, Toward Achieving Formal Guarantees for Human-Aware Controllers in Human-Robot Interactions, Submitted, 2019
This work explores the use of formal methods to construct human-aware robot controllers to support the productivity requirements of humans. We tackle these types of scenarios via human workload-informed models and reactive synthesis. This strategy allows us to synthesize controllers that fulfill formal specifications that are expressed as linear temporal logic formulas.
Here, we advance on real-time grasping pose estimation of single or multiple handles from RGB-D images, providing a speed up for assistive human-centered behaviors. We propose a versatile Bayesian framework that endows robots with the ability to infer various door kinematic models from observations of its motion. Combining this probabilistic approach with a state-of-the- art motion planner, we achieve efficient door grasping and subsequent door operation regardless of the kinematic model using the Toyota Human Support Robot.
arXiv Preprint: M. Arduengo, C. Torras, L. Sentis, A Versatile Framework for Robust and Adaptive Door Operation with a Mobile Manipulator Robot
Click below to view the PDF of our work titled: Dynamic Locomotion For Passive-Ankle Biped Robots And Humanoids Using Whole-Body Locomotion Control.
