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
REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robots
Andrea Tagliabue, Kota Kondo, Tong Zhao, Mason Peterson, Claudius T. Tewari, Jonathan P. How
GREEMA: Proposal and Experimental Verification of Growing Robot by Eating Environmental MAterial for Landslide Disaster
Yusuke Tsunoda, Yuya Sato, Koichi Osuka
Integration of Large Language Models within Cognitive Architectures for Autonomous Robots
Miguel Á. González-Santamarta, Francisco J. Rodríguez-Lera, Ángel Manuel Guerrero-Higueras, Vicente Matellán-Olivera
Interaction-Aware Sampling-Based MPC with Learned Local Goal Predictions
Walter Jansma, Elia Trevisan, Álvaro Serra-Gómez, Javier Alonso-Mora