IRIS: Learning-Driven Task-Specific Cinema Robot Arm for Visuomotor Motion Control
AI 摘要
IRIS:低成本、学习驱动的电影机器人手臂,实现自主的视觉运动控制。
主要贡献
- 设计了一种低成本的6自由度机器人手臂
- 提出了基于Transformer的动作块的视觉运动模仿学习框架
- 验证了系统在各种电影运动中的准确性和泛化性
方法论
利用模仿学习,从人类演示中学习目标导向的、平滑的相机轨迹,无需显式的几何编程。
原文摘要
Robotic camera systems enable dynamic, repeatable motion beyond human capabilities, yet their adoption remains limited by the high cost and operational complexity of industrial-grade platforms. We present the Intelligent Robotic Imaging System (IRIS), a task-specific 6-DOF manipulator designed for autonomous, learning-driven cinematic motion control. IRIS integrates a lightweight, fully 3D-printed hardware design with a goal-conditioned visuomotor imitation learning framework based on Action Chunking with Transformers (ACT). The system learns object-aware and perceptually smooth camera trajectories directly from human demonstrations, eliminating the need for explicit geometric programming. The complete platform costs under $1,000 USD, supports a 1.5 kg payload, and achieves approximately 1 mm repeatability. Real-world experiments demonstrate accurate trajectory tracking, reliable autonomous execution, and generalization across diverse cinematic motions.