AnyTask: Sim-to-Realポリシー学習を進めるための自動タスク・データ生成フレームワーク
AnyTask: an Automated Task and Data Generation Framework for Advancing Sim-to-Real Policy Learning
GPUシミュレーションと基盤モデルを組み合わせ、多様なマニピュレーションタスクの設計から専門家デモ生成、実機転移までを自動化するフレームワークを提案。生成データで学習したポリシーが実機で平均44%の成功率を達成。
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: Ran Gong, Xiaohan Zhang, Jinghuan Shang, Maria Vittoria Minniti, Jigarkumar Patel, Valerio Pepe, Riedana Yan, Ahmet Gundogdu, Ivan Kapelyukh, Ali Abbas, Xiaoqiang Yan, Harsh Patel, Laura Herlant, Karl Schmeckpeper
分類: cs.RO, cs.AI
原文アブストラクト
Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has become a promising way for scaling up data collection, the related tasks, including simulation task design, task-aware scene generation, expert demonstration synthesis, and sim-to-real transfer, still demand substantial human effort. We present AnyTask, an automated framework that pairs massively parallel GPU simulation with foundation models to design diverse manipulation tasks and synthesize robot data. We introduce three AnyTask agents for generating expert demonstrations aiming to solve as many tasks as possible: 1) ViPR, a novel task and motion planning agent with VLM-in-the-loop Parallel Refinement; 2) ViPR-Eureka, a reinforcement learning agent with generated dense rewards and LLM-guided contact sampling; 3) ViPR-RL, a hybrid planning and learning approach that jointly produces high-quality demonstrations with only sparse rewards. We train behavior cloning policies on generated data, validate them in simulation, and deploy them directly on real robot hardware. The policies generalize to novel object poses, achieving 44% average success across a suite of real-world pick-and-place, drawer opening, contact-rich pushing, and long-horizon manipulation tasks. Our project website is at https://anytask.rai-inst.com .
