Abstract
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 this https URL .
Abstract (translated)
通用型机器人学习仍受数据限制：在现实世界中收集大规模、多样化和高质量的交互数据成本高昂。尽管仿真已经成为扩大数据采集的一种有前景的方法，包括任务设计、感知任务的场景生成、专家演示合成以及从仿真到实际环境的数据转移等相关的任务仍然需要大量的手工劳动。我们提出了AnyTask，这是一个自动化的框架，它结合了大规模并行GPU仿真和基础模型来设计多样化的操作任务，并合成人机交互数据。我们介绍了三种用于生成旨在解决尽可能多的任务的专家演示的AnyTask代理：1）ViPR，一种新颖的任务与运动规划代理，采用了视觉语言模型循环中的平行细化；2）ViPR-Eureka，一个强化学习代理，具有生成密集奖励和大型语言模型引导接触采样的功能；3）ViPR-RL，一种混合型计划和学习方法，仅用稀疏奖励就能产生高质量的演示。我们在合成数据上训练行为克隆策略，在仿真中验证这些策略，并直接部署在真实的机器人硬件上。所训练的政策能够泛化到新的物体姿态，实现在一系列真实世界的抓取放置、抽屉打开、接触密集型推动以及长时程操作任务中的平均44%的成功率。 我们项目的官方网站位于[这里](https://this-is-the-url-of-your-project-website.com)（请将URL替换为实际项目网站地址）。
URL
https://arxiv.org/abs/2512.17853
