Environment Exploration
Environment exploration in robotics and AI focuses on enabling agents to efficiently and effectively learn about and navigate unknown environments, optimizing for factors like map creation, sensor fusion, and efficient decision-making. Current research emphasizes leveraging deep learning models, such as neural networks and transformers, for tasks like map prediction, sensor calibration (e.g., LiDAR-camera), and skill acquisition, often incorporating techniques like reinforcement learning and information gain calculations to guide exploration strategies. These advancements have implications for various fields, including autonomous navigation, game design, and personalized healthcare, by improving the robustness and adaptability of AI agents in complex and dynamic settings.
Papers
The Vocal Signature of Social Anxiety: Exploration using Hypothesis-Testing and Machine-Learning Approaches
Or Alon-Ronen, Yosi Shrem, Yossi Keshet, Eva Gilboa-Schechtman
Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy Bischoff, Tie-Yan Liu
BYOL-Explore: Exploration by Bootstrapped Prediction
Zhaohan Daniel Guo, Shantanu Thakoor, Miruna Pîslar, Bernardo Avila Pires, Florent Altché, Corentin Tallec, Alaa Saade, Daniele Calandriello, Jean-Bastien Grill, Yunhao Tang, Michal Valko, Rémi Munos, Mohammad Gheshlaghi Azar, Bilal Piot
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Hiroaki Funayama, Tasuku Sato, Yuichiroh Matsubayashi, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui