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
Foundations of Spatial Perception for Robotics: Hierarchical Representations and Real-time Systems
Nathan Hughes, Yun Chang, Siyi Hu, Rajat Talak, Rumaisa Abdulhai, Jared Strader, Luca Carlone
Using a Bayesian-Inference Approach to Calibrating Models for Simulation in Robotics
Huzaifa Mustafa Unjhawala, Ruochun Zhang, Wei Hu, Jinlong Wu, Radu Serban, Dan Negrut
Computer-Vision Based Real Time Waypoint Generation for Autonomous Vineyard Navigation with Quadruped Robots
Lee Milburn, Juan Gamba, Miguel Fernandes, Claudio Semini
A New Wave in Robotics: Survey on Recent mmWave Radar Applications in Robotics
Kyle Harlow, Hyesu Jang, Timothy D. Barfoot, Ayoung Kim, Christoffer Heckman