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
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang
Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning
Xuankai Chang, Brian Yan, Yuya Fujita, Takashi Maekaku, Shinji Watanabe
Leveraging Language Representation for Material Recommendation, Ranking, and Exploration
Jiaxing Qu, Yuxuan Richard Xie, Kamil M. Ciesielski, Claire E. Porter, Eric S. Toberer, Elif Ertekin
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Remi Munos, Will Dabney, Diana L Borsa