用示范学习让地面机器人自动拍出流畅的推镜头，无需设计奖励函数。
Learning Dolly-In Filming From Demonstration Using a Ground-Based Robot
- 通过模仿专家操作轨迹训练生成对抗式模仿模型，实现免奖励函数控制。
- 在仿真中表现优于PPO基线，收敛更快、方差更低，且直接迁移到真实机器人。
- 适合影视创作者快速实现风格化运镜，降低技术门槛。
电影级摄像控制需要精度与艺术性的平衡，而传统手工设计奖励函数难以实现。尽管强化学习已用于机器人摄制，但其依赖定制化奖励和大量调参，限制了创意应用。本文提出一种基于示范学习（LfD）的方法，采用生成对抗式模仿学习（GAIL），使用自由移动的地基拍摄机器人自动化完成推镜头。专家轨迹通过模拟环境中的操纵杆远程操控采集，捕捉平滑、富有表现力的运动，无需显式目标设计。仅基于这些示范训练的GAIL策略，在仿真中表现优于PPO基线，获得更高奖励、更快收敛速度和更低方差。关键的是，该策略无需微调即可直接部署于真实机器人，实现比先前TD3方法更一致的画面构图与主体对齐。结果表明，示范学习为电影领域提供了鲁棒、免奖励的强化学习替代方案，支持实时部署且技术门槛极低。本流程使创意从业者能够便捷实现风格化摄像控制，弥合艺术意图与机器人自主之间的鸿沟。
原文摘要 · Abstract (English)
Cinematic camera control demands a balance of precision and artistry - qualities that are difficult to encode through handcrafted reward functions. While reinforcement learning (RL) has been applied to robotic filmmaking, its reliance on bespoke rewards and extensive tuning limits creative usability. We propose a Learning from Demonstration (LfD) approach using Generative Adversarial Imitation Learning (GAIL) to automate dolly-in shots with a free-roaming, ground-based filming robot. Expert trajectories are collected via joystick teleoperation in simulation, capturing smooth, expressive motion without explicit objective design. Trained exclusively on these demonstrations, our GAIL policy outperforms a PPO baseline in simulation, achieving higher rewards, faster convergence, and lower variance. Crucially, it transfers directly to a real-world robot without fine-tuning, achieving more consistent framing and subject alignment than a prior TD3-based method. These results show that LfD offers a robust, reward-free alternative to RL in cinematic domains, enabling real-time deployment with minimal technical effort. Our pipeline brings intuitive, stylized camera control within reach of creative professionals, bridging the gap between artistic intent and robotic autonomy.
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