# Learning Dolly-In Filming From Demonstration Using a Ground-Based Robot

## TL;DR

This paper introduces a demonstration-based learning approach using GAIL for autonomous cinematic camera control with a ground robot, outperforming traditional RL methods and enabling real-world deployment without extensive tuning.

## Contribution

It presents a novel GAIL-based imitation learning method for robotic filmmaking that transfers from simulation to real-world without fine-tuning, reducing technical effort and enhancing creative control.

## Key findings

- GAIL policy outperforms PPO in simulation with higher rewards and faster convergence.
- The learned policy transfers directly to real robots, outperforming previous methods.
- The approach enables real-time, stylized camera control for filmmakers with minimal setup.

## Abstract

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.