Jacta：一种用于学习灵巧与全身操作的多功能规划器
Jacta: A Versatile Planner for Learning Dexterous and Whole-body Manipulation
AI总结：
本文提出灵活运动规划器 Jacta，为强化学习生成可直接使用的演示，从而高效学习灵巧与全身操作策略，并验证其可迁移至真实机器人。
摘要：
由于不连续动力学以及高维状态和动作空间，机器人操作颇具挑战。在操作任务中取得成功的数据驱动方法需要大量数据和专家演示，这些通常来自人类。现有规划器受限于特定系统，并且往往依赖专门算法来使用演示。因此，我们提出一种为灵巧和全身操作任务定制的灵活运动规划器。我们的规划器可为强化学习算法生成易于使用的演示，无需额外复杂的训练流程。通过这种方法，我们能够高效学习复杂操作任务的策略，而仅靠传统强化学习在这些任务上只能取得微小进展。此外，我们证明所学策略可迁移到真实机器人系统，用于解决复杂的灵巧操作任务。项目网站：https://jacta-manipulation.github.io/
英文摘要：
Robotic manipulation is challenging due to discontinuous dynamics, as well as high-dimensional state and action spaces. Data-driven approaches that succeed in manipulation tasks require large amounts of data and expert demonstrations, typically from humans. Existing planners are restricted to specific systems and often depend on specialized algorithms for using demonstrations. Therefore, we introduce a flexible motion planner tailored to dexterous and whole-body manipulation tasks. Our planner creates readily usable demonstrations for reinforcement learning algorithms, eliminating the need for additional training pipeline complexities. With this approach, we can efficiently learn policies for complex manipulation tasks, where traditional reinforcement learning alone only makes little progress. Furthermore, we demonstrate that learned policies are transferable to real robotic systems for solving complex dexterous manipulation tasks. Project website: https://jacta-manipulation.github.io/
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。
