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
Mesh2SLAM in VR: A Fast Geometry-Based SLAM Framework for Rapid Prototyping in Virtual Reality Applications
Carlos Augusto Pinheiro de Sousa, Heiko Hamann, Oliver Deussen
Sensorimotor Control Strategies for Tactile Robotics
Enrico Donato, Matteo Lo Preti, Lucia Beccai, Egidio Falotico
RoboReflect: Robotic Reflective Reasoning for Grasping Ambiguous-Condition Objects
Zhen Luo, Yixuan Yang, Chang Cai, Yanfu Zhang, Feng Zheng
Toward Information Theoretic Active Inverse Reinforcement Learning
Ondrej Bajgar, Sid William Gould, Rohan Narayan Langford Mitta, Jonathon Liu, Oliver Newcombe, Jack Golden
Predicate Invention from Pixels via Pretrained Vision-Language Models
Ashay Athalye, Nishanth Kumar, Tom Silver, Yichao Liang, Tomás Lozano-Pérez, Leslie Pack Kaelbling