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 Exploration of Knowledge-Preserving Prompts for Document Summarisation
Chen Chen, Wei Emma Zhang, Alireza Seyed Shakeri, Makhmoor Fiza
ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration
Yuhong Cao, Tianxiang Hou, Yizhuo Wang, Xian Yi, Guillaume Sartoretti
Flexible Supervised Autonomy for Exploration in Subterranean Environments
Harel Biggie, Eugene R. Rush, Danny G. Riley, Shakeeb Ahmad, Michael T. Ohradzansky, Kyle Harlow, Michael J. Miles, Daniel Torres, Steve McGuire, Eric W. Frew, Christoffer Heckman, J. Sean Humbert
Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots
Yang Xu, Ronghao Zheng, Senlin Zhang, Meiqin Liu