Efficient Imitation Without Demonstrations via Value-Penalized Auxiliary Control from Examples
Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS)（多伦多大学航空航天研究所（UTIAS）空间与地面自主机器人系统（STARS）实验室）；Department of Computing Science at the University of Alberta（阿尔伯塔大学计算机科学系）
Trevor Ablett, Bryan Chan, Jayce Haoran Wang, Jonathan Kelly
Common approaches to providing feedback in reinforcement learning are the use of hand-crafted rewards or full-trajectory expert demonstrations. Alternatively, one can use examples of completed tasks, but such an approach can be extremely sample inefficient. We introduce value-penalized auxiliary control from examples (VPACE), an algorithm that significantly improves exploration in example-based control by adding examples of simple auxiliary tasks and an above-success-level value penalty. Across both simulated and real robotic environments, we show that our approach substantially improves learning efficiency for challenging tasks, while maintaining bounded value estimates. Preliminary results also suggest that VPACE may learn more efficiently than the more common approaches of using full trajectories or true sparse rewards. Project site: https://papers.starslab.ca/vpace/.
Trajectory Planning for UAV-Based Smart Farming Using Imitation-Based Triple Deep Q-Learning
National Institute of Informatics（国立信息研究所）；The University of Tokyo（东京大学）；Pontificia Universidad Católica de Chile（智利天主教大学）；China Agricultural University（中国农业大学）
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline Data
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Artificial Intelligence Research Institute（四川省人工智能研究院）；Duke Kunshan University（昆山杜克大学）
Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；National University of Defense Technology（国防科技大学）；University of Maryland, College Park（马里兰大学帕克分校）
Beyond Imitation: Reinforcement Learning-Based Sim-Real Co-Training for VLA Models
Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Peking University（北京大学）；Carnegie Mellon University（卡内基梅隆大学）；Shanghai AI Laboratory（上海人工智能实验室）；Zhongguancun Academy（中关村学院）
GravMAD: Grounded Spatial Value Maps Guided Action Diffusion for Generalized 3D Manipulation
State Key Laboratory for Novel Software Technology, Nanjing University（南京大学计算机软件新技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University (Suzhou Campus)（南京大学智能科学与技术学院（苏州校区））；School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）
