论文标题
通过基于GPU的模拟和高质量的演示，加速类似人类的人类操纵学习
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations
论文作者
论文摘要
使用拟人化机器人手的灵巧操作在机器人技术中仍然是一个具有挑战性的问题，因为高维状态和动作空间以及复杂的接触。然而，需要熟练的闭环操作才能使类人形机器人能够在非结构化的现实世界环境中运行。加强学习（RL）传统上提出了巨大的交互数据要求，以优化此类复杂的控制问题。我们介绍了一个新的框架，该框架利用基于GPU的模拟的最新进展以及模仿学习的实力指导政策搜索以有希望的行为，以使RL培训在这些领域中可行。为此，我们提出了一个沉浸式的虚拟现实界面界面，旨在在接触良好的任务和一套受日常生活任务启发的操纵环境中进行交互式的操纵和一套操纵环境。最后，我们证明了大规模平行的RL和模仿学习的互补优势，从而产生了强大而自然的行为。训练有素的策略，我们的源代码和收集的演示数据集的视频可在https://maltemosbach.github.io/interactive_human_like_manipulation/。
Dexterous manipulation with anthropomorphic robot hands remains a challenging problem in robotics because of the high-dimensional state and action spaces and complex contacts. Nevertheless, skillful closed-loop manipulation is required to enable humanoid robots to operate in unstructured real-world environments. Reinforcement learning (RL) has traditionally imposed enormous interaction data requirements for optimizing such complex control problems. We introduce a new framework that leverages recent advances in GPU-based simulation along with the strength of imitation learning in guiding policy search towards promising behaviors to make RL training feasible in these domains. To this end, we present an immersive virtual reality teleoperation interface designed for interactive human-like manipulation on contact rich tasks and a suite of manipulation environments inspired by tasks of daily living. Finally, we demonstrate the complementary strengths of massively parallel RL and imitation learning, yielding robust and natural behaviors. Videos of trained policies, our source code, and the collected demonstration datasets are available at https://maltemosbach.github.io/interactive_ human_like_manipulation/.
