CMU, Meta AI Researchers Develop Robotic Learning Agent That Can Master Multiple Skills RoboAgent can complete 12 manipulation skills across differing scenes. This research points toward a robotic learning platform adaptable to changing environments. Humans are social creatures and learn from each other, even from[...]

CMU's Autonomous Exploration Research Team has developed a suite of robotic systems and planners enabling robots to explore more quickly, probe the darkest corners of unknown environments, and create more accurate and detailed maps — all without human help. A research group in[...]

Researchers at CMU and the Israel Institute of Technology’s Technion have created 3D sensors that capture micron-scale depth detail, like the raised height of Washington’s head on a U.S. quarter. Ever wonder what the raised height of Washington's head is on a U.S. quarter? Or[...]

Dynamic Light Field Network (DyLiN) accommodates dynamic scene deformations, such as in avatar animation, where facial expressions can be used as controllable input attributes. Researchers from Carnegie Mellon University and Fujitsu have developed a new method to convert 2D images to a 3D structure. The[...]

Congratulations to members of Changliu Liu's Intelligent Control Lab Alvin Shek, Bo Ying Su and Rui Chen for winning the 2023 ICRA Outstanding Interaction Paper Award! The paper "Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method" received the 2023 ICRA Outstanding Interaction Paper[...]

Congratulations to Wennie Tabib, Kshitij Goel, John Yao, Curtis Boirum, and Nathan Michael for receiving an honorable mention for the IEEE T-RO King-Sun Fu Memorial Best Paper Award at the 2023 RAS Award Ceremony in London, UK! This award is for their work "Autonomous Cave[...]