CycleManip: 通过有效的历史感知和理解实现循环任务操控
CycleManip: Enabling Cyclic Task Manipulation via Effective Historical Perception and Understanding
AI总结：
本文提出CycleManip框架，通过端到端模仿方法实现循环任务操控，解决了历史利用不足和缺乏评估基准的问题，实验表明方法在模拟和现实环境中均表现出高成功率和良好的适应性。
摘要：
在本文中，我们探索了机器人操控中一个重要但尚未被充分研究的任务：基于循环的任务操控，其中机器人需要在预期终端时间内执行循环或重复动作。这些任务在日常生活中至关重要，例如摇瓶子或敲钉子。然而，很少有先前工作探索了这一任务，导致两个主要挑战：1）模仿方法往往无法在预期终端时间内完成这些任务，因为历史利用无效；2）缺乏具有足够数据和自动评估工具的基准，阻碍了该领域有效解决方案的发展。为了解决这些挑战，我们首先提出了CycleManip框架，以端到端模仿方式实现循环任务操控，而无需任何额外模型、层次结构或显著计算开销。核心见解是通过成本感知采样策略增强有效历史感知，并通过多任务学习改进历史理解。其次，我们引入了一个基于循环的任务操控基准，提供了多样化的循环任务和自动评估方法。在模拟和现实环境中的大量实验表明，我们的方法在循环任务操控中实现了高成功率。结果进一步显示了在一般操控中的强大适应性和在模仿策略如视觉-语言-动作（VLA）模型上的即插即用能力。此外，结果表明我们的方法可以应用于多种机器人平台，包括双臂夹具、灵活手和人形机器人。
英文摘要：
In this paper, we explore an important yet underexplored task in robot manipulation: cycle-based manipulation, where robots need to perform cyclic or repetitive actions with an expected terminal time. These tasks are crucial in daily life, such as shaking a bottle or knocking a nail. However, few prior works have explored this task, leading to two main challenges: 1) the imitation methods often fail to complete these tasks within the expected terminal time due to the ineffective utilization of history; 2) the absence of a benchmark with sufficient data and automatic evaluation tools hinders development of effective solutions in this area. To address these challenges, we first propose the CycleManip framework to achieve cycle-based task manipulation in an end-to-end imitation manner without requiring any extra models, hierarchical structure or significant computational overhead. The core insight is to enhance effective history perception by a cost-aware sampling strategy and to improve historical understanding by multi-task learning. Second, we introduce a cycle-based task manipulation benchmark, which provides diverse cycle-based tasks, and an automatic evaluation method. Extensive experiments conducted in both simulation and real-world settings demonstrate that our method achieves high success rates in cycle-based task manipulation. The results further show strong adaptability performance in general manipulation, and the plug-and-play ability on imitation policies such as Vision-Language-Action (VLA) models. Moreover, the results show that our approach can be applied across diverse robotic platforms, including bi-arm grippers, dexterous hands, and humanoid robots.
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。
