IRIS: Learning-Driven Task-Specific Cinema Robot Arm for Visuomotor Motion Control

TL;DR

IRIS is an affordable, learning-driven robotic camera system that autonomously generates smooth, object-aware cinematic motions from human demonstrations, reducing complexity and cost in robotic cinematography.

Contribution

The paper introduces IRIS, a low-cost, task-specific robotic camera arm utilizing a novel visuomotor imitation learning framework with Action Chunking Transformers.

Findings

Achieves approximately 1 mm repeatability in motion control.

Supports autonomous, object-aware cinematic camera trajectories.

Costs under $1,000 USD and supports a 1.5 kg payload.

Abstract

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.