Robotics Domain
Robotics research currently focuses on enhancing robot autonomy, safety, and dexterity, particularly in unstructured environments. Key areas include developing robust control algorithms (like Model Predictive Control and reinforcement learning), improving perception through advanced sensor fusion and generative models, and creating more efficient and adaptable robot designs. These advancements are driving progress in diverse applications such as agriculture, healthcare, and manufacturing, ultimately aiming to create more capable and reliable robots for a wider range of tasks.
Papers
Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks
Yuanhong Zeng, Yizhou Zhao, Ying Nian Wu
Pour me a drink: Robotic Precision Pouring Carbonated Beverages into Transparent Containers
Feiya Zhu, Shuo Hu, Letian Leng, Alison Bartsch, Abraham George, Amir Barati Farimani
Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics
Jiayang Song, Zhehua Zhou, Jiawei Liu, Chunrong Fang, Zhan Shu, Lei Ma
Exp[licit]-A Robot modeling Software based on Exponential Maps
Johannes Lachner, Moses C. Nah, Stefano Stramigioli, Neville Hogan