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.
In this video, PI Luis Sentis talks about efficient actuation approaches for human-centered robots:
Congratulations to Ce Xu, our recent student co-advised with the UPC for devising new methods to learn and predict the motion of soft materials such as towels using mixture of Gaussian Processes. More details will be available soon.
The HCRL is proud to share our new NSF National Research Traineeship on Ethical AI Training for Roboticists. Congratulations to all the participants and to the Good Systems Bridging Barriers Program.
Congratulations to Junhyeok Ahn, Nicolas Brissonneau, Bingham He from the HCRL, and Nick Paine, Orion Campbell, and Nick Nichols, from Apptronik for this great work on augmenting forces of operators using full lower-body exoskeletons.
Our collaborative NSF National Research Traineeship grant has been awarded! Dubbed, the NRT-AI: Convergent, Responsible, and Ethical AI Training Experience for Roboticists (CREATE Roboticists) will fund and train multiple generations of PhD students at the intersection between robotics, autonomous systems and ethics. It will create a new portfolio program in ethical robotics to ensure that technology and academic leaders coming from this program have gained expertise and education towards the good use of robotics from a social and labor perspective. We will join forces with faculty across various departments at UT to create a unique program with diverse perspectives.
We have accomplished strength amplification using Apptronik’s SAGIT exoskeleton reducing significantly the effort of operators carrying payloads and performing repetitive movements.
Draco 3 is up and swinging using our motion planning and control software PnC.
Here, Junhyeok describes our work on training nested mixture of expert mdoels for data-efficient model-based reinforcement learning. In particular, we focus on learning mixture of expert representations of hybrid dynamical systems that engage in intermittent contacts, such as legged robots. You can access the paper by using the URL below:
http://proceedings.mlr.press/v144/ahn21a/ahn21a.pdf
