AIIRA’s vision is to create new AI-driven, predictive digital twins for modeling plants, and deploy them to increase the resiliency of the nation’s agricultural systems. AIIRA's institutions cover three of the five major areas of the US mainland, including Midwest, Southwest, and Northeast with stakeholders composed of companies, governmental agencies, commodity groups, prominent centers, and [...]
The Safe AI Lab at CMU aims to develop reliable, explainable, verifiable, and good-for-all artificial intelligent learning methods for consequential applications in the face of the uncertain, dynamic, time-varying, multiple agents, and possibly human-involved environment by bridging fundamental learning theories and engineering technologies.
The Kantor Lab’s goal is to develop techniques for perceiving and manipulating plants using robots. The potential applications are broad, and include fruit detection and picking, yield prediction, health assessment, pruning, and crop management, among others. The lab currently has projects spanning outdoor interactions with field crops, indoor work with specialty crops, and drone flights [...]
The DROP (Deep Robot Optical Perception) Lab's research goal is to develop robotic systems capable of operating in complex dynamic environments. To this end, we seek to expand and improve the perceptual capabilities of autonomous systems. Our work seeks to push the bounds of scale and resolution in 3D reconstruction, segmentation, data mining, and visualization [...]
For more information, please visit Laszlo Jeni's CUBE Lab Site.
Please visit our lab site to learn more.
We develop new computational, control, and sensing methods for robots to physically interact with and assist people. We bring together researchers that explore the intersection of physically assistive robotics, machine learning, physics simulation, perception, and human-robot interaction. Our research often has applications in healthcare robotics, including the advancement of robotic caregivers -- robots that assist [...]
Our lab works to develop new methods for robot perception and control. We are focusing on deformable object manipulation, autonomous driving, and safe reinforcement learning.