HolidayFactory 1.0：産業用ロボット作業のシミュレーション・生成・評価フレームワーク
HolidayFactory 1.0: Simulating, Generating, and Evaluating Industrial Robot Tasks
産業用ロボットの反復作業をリセットなしで大規模評価するため、接触・アクチュエータ・触覚モデルを備えたシミュレータと、シーン構築の自動生成、7種の産業タスクベンチマークを統合したフレームワークを提案。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Holiday Robotics, :, Jeongmin Lee, Taehyeon Kim, Dohyun Jang, Minji Lee, Seonghun Oh, Dongwoo Park, Geonhyup Lee, Jaehyung Kim, Jaemin Yoon, Jinseop Bok, Jungwoo Kim, Minseok Yang, Philsik Chang, Soomin Lee
分類: cs.RO, cs.AI
原文アブストラクト
Robots are typically evaluated in episodes that begin from a reset, while industrial work consists of repeated jobs whose outcomes affect subsequent operations. Evaluating such work at scale requires simulation of task-critical physical interactions, construction of industrial environments, and measurements of performance as scene state persists. We present HOLIDAYFACTORY 1.0, a framework for simulating, generating, and evaluating industrial robot work. Given a human-specified workplace and task, the framework defines a world pairing a scenario of scene variants and process logic with an evaluation defining a job, metrics, and a rubric. HOLIDAYFACTORY 1.0 comprises three components. HOLIDAY-SIM extends Newton's SDF-based hydroelastic contact with additional constitutive laws and a semi-implicit primal-dual solver, couples stateful actuator dynamics to articulated motion, and derives tactile and joint observations from simulated states. Agentic world construction generates simulation-ready assets and scene variants using geometric and interaction checks, and proposes task-conditioned evaluations for human confirmation and executable checks. 24/7-BENCH introduces seven industrial tasks in which jobs are evaluated without resetting the scene, measuring completed, failed, and unfinished jobs, throughput, latency, and motor energy. Controlled studies characterize the proposed contact, actuator, and tactile models and examine the effectiveness of verification-guided world construction. Experiments on scene and evaluation generation further reveal the limitations of model critics and the difficulty of ensuring that generated evaluations faithfully capture task requirements. We examine job-level evaluations with scripted trajectories (e.g., code-as-policy) but have not yet compared policies over persistent runs.