基于强化学习的可扩展多任务数据生成用于语言条件双手灵巧操作
Scalable Multi-Task Data Generation via Reinforcement Learning for Language-Conditioned Bimanual Dexterous Manipulation
AI总结：
提出基于强化学习的系统化数据生成流程，结合通用奖励设计、域随机化和语言条件任务标注，合成高质量双手灵巧操作数据集，训练语言条件多任务策略，提升跨任务泛化能力。
摘要：
训练双手灵巧操作的通用策略的一个关键瓶颈是缺乏大规模、高质量的数据集。模拟中的合成数据生成通过克服形态不匹配、缺失物理交互和机器人动作生成等挑战，为人类视频演示提供了可扩展的替代方案。然而，现有基于人类遥操作的方法任务多样性有限，因为以物体为中心的轨迹匹配常常忽略机器人执行的可行性。强化学习（RL）提供了更广泛的可扩展性，但通常受限于手工设计的、特定任务的奖励。在这项工作中，我们提出了一种基于RL的系统化数据生成流程，集成了通用奖励设计、有效的域随机化和语言条件任务标注。该流程为灵巧双手操作合成了多样化、高质量的数据集，并支持训练语言条件多任务策略。我们的实验表明，生成的数据显著提高了三个代表性操作任务的泛化能力。
英文摘要：
A key bottleneck in training generalist policies for bimanual dexterous manipulation is the lack of large-scale, high-quality datasets. Synthetic data generation in simulation provides a scalable alternative to human video demonstrations by overcoming challenges such as morphology mismatch, missing physical interactions, and the generation of robot actions. However, existing approaches based on human teleoperation offer limited task diversity, as object-centric trajectory matching often neglects the feasibility of robot execution. Reinforcement learning (RL) enables broader scalability but is often constrained by handcrafted, task-specific rewards. In this work, we propose a systematic RL-based data generation pipeline that integrates generalizable reward design, effective domain randomization, and language-conditioned task annotations. This pipeline synthesizes diverse, high-quality datasets for dexterous bimanual manipulation and enables training of language-conditioned multi-task policies. Our experiments show that the generated data significantly improves generalization across three representative manipulation tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
