RL是怎么赋能VLA的？

Core Insights - The most popular direction this year is VLA+RL, which introduces a new interaction paradigm for embodied intelligence, allowing robots to perceive environments visually, understand language instructions, and generate action sequences directly [1] - VLA models face challenges such as execution instability and sensitivity to initial states, which can lead to failures in long-sequence tasks; reinforcement learning (RL) offers a solution by providing a mechanism for closed-loop optimization of action strategies [2][4] - Current research trends are shifting from merely training VLA models to using VLA as a policy representation combined with RL for fine-tuning and enhancement [5] Research Trends - The integration of VLA and RL is becoming a default combination rather than an optional one, indicating a growing interest in this research area [8] - There are significant challenges in applying RL within VLA systems, as many researchers lack practical experience with real machines and RL [10] - The course aims to guide students in the VLA+RL field, focusing on developing independent research capabilities rather than just reiterating existing papers [31] Course Structure - The course consists of 14 weeks of intensive guidance, covering topics such as the challenges of embodied intelligence, VLA model foundations, RL basics, and the integration of RL in VLA systems [15][31] - Specific weeks focus on practical aspects like simulation platform setup, long-term task challenges, and memory mechanisms [20][24] - The course also emphasizes academic skills, including writing and research methodology, to help students produce high-quality academic papers [31][34] Expected Outcomes - Upon completion, students will have a comprehensive understanding of the theoretical foundations and technical evolution of VLA+RL, the ability to conduct independent research, and the skills to write and submit academic papers [34]