Autonomous Robot
Autonomous robots are being developed to perform complex tasks in diverse and unpredictable environments, focusing on robust navigation, adaptive behavior, and reliable operation. Current research emphasizes improving perception through advanced computer vision techniques (e.g., keypoint detection, neural radiance fields) and developing efficient planning algorithms (e.g., deep reinforcement learning, hierarchical decision networks, belief space search) that incorporate uncertainty and handle dynamic situations. These advancements are significant for various applications, including exploration, manufacturing, agriculture, and search and rescue, promising increased efficiency and safety in challenging real-world scenarios.
Papers
Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes
Friedrich M. Rockenbauer, Jaeyoung Lim, Marcus G. Müller, Roland Siegwart, Lukas Schmid
CoBL-Diffusion: Diffusion-Based Conditional Robot Planning in Dynamic Environments Using Control Barrier and Lyapunov Functions
Kazuki Mizuta, Karen Leung
DexSkills: Skill Segmentation Using Haptic Data for Learning Autonomous Long-Horizon Robotic Manipulation Tasks
Xiaofeng Mao, Gabriele Giudici, Claudio Coppola, Kaspar Althoefer, Ildar Farkhatdinov, Zhibin Li, Lorenzo Jamone
Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery
Kento Kawaharazuka, Kei Okada, Masayuki Inaba