Title: Reinforcement Learning of Dolly-In Filming Using a Ground-Based Robot Show Chinese title

Title: 基于地面机器人的拍摄中Dolly-In的强化学习

Abstract: Free-roaming dollies enhance filmmaking with dynamic movement, but challenges in automated camera control remain unresolved. Our study advances this field by applying Reinforcement Learning (RL) to automate dolly-in shots using free-roaming ground-based filming robots, overcoming traditional control hurdles. We demonstrate the effectiveness of combined control for precise film tasks by comparing it to independent control strategies. Our robust RL pipeline surpasses traditional Proportional-Derivative controller performance in simulation and proves its efficacy in real-world tests on a modified ROSBot 2.0 platform equipped with a camera turret. This validates our approach's practicality and sets the stage for further research in complex filming scenarios, contributing significantly to the fusion of technology with cinematic creativity. This work presents a leap forward in the field and opens new avenues for research and development, effectively bridging the gap between technological advancement and creative filmmaking. Show Chinese abstract

Abstract: 自由漫游的滑轨增强了电影制作中的动态运动，但自动化摄像机控制方面的挑战仍未解决。 我们的研究通过应用强化学习（RL）来自动控制滑轨镜头，推动了这一领域的发展，克服了传统的控制障碍。 我们通过与独立控制策略进行比较，展示了结合控制在精确电影任务中的有效性。 我们的鲁棒强化学习流程在仿真中超越了传统比例-微分控制器的性能，并在配备摄像头塔的修改版ROSBot 2.0平台上进行了实际测试，证明了其有效性。 这验证了我们方法的实用性，并为复杂拍摄场景的进一步研究奠定了基础，对技术与电影创意的融合做出了重要贡献。 这项工作在该领域取得了重大进展，并为研究和开发开辟了新的途径，有效地弥合了技术进步与创造性电影制作之间的差距。