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arXiv:2602.17537

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

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

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著者: Qilong Cheng, Matthew Mackay, Ali Bereyhi

分類: cs.RO, cs.LG

原文アブストラクト

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.