解构感知与推理以提高无演示学习布料操控的数据效率
Disentangling perception and reasoning for improving data efficiency in learning cloth manipulation without demonstrations
AI总结：
本文提出了一种高效且模块化的强化学习方法，通过减少模型大小和训练时间来提高布料操控学习的数据效率，并在现实世界中实现有效迁移。
摘要：
布料操控是日常生活中的普遍任务，但仍然是机器人领域的一个开放性挑战。开发布料操控策略的困难归因于高维状态空间、复杂动态以及织物表现出的高自我遮挡性。由于分析方法无法提供稳健且通用的操控策略，强化学习（RL）被视为解决这些问题的有前途的方法。然而，为了应对大规模状态空间和复杂动态，数据驱动的方法通常依赖于大型模型和长时间训练。由此产生的计算成本显著阻碍了这些方法的发展和应用。此外，由于稳健状态估计的挑战，服装操控策略通常采用端到端学习方法，以工作空间图像作为输入。虽然这种方法通过现实世界微调实现了概念上简单的仿真到现实的迁移，但也通过在高度损失性环境中训练智能体而带来了显著的计算成本。本文质疑这一常见设计选择，通过探索布料操控的高效且模块化的RL方法。我们表明，通过精心的设计选择，学习仿真时模型大小和训练时间可以显著减少。此外，我们展示了由此产生的仿真训练模型如何转移到现实世界。我们在SoftGym基准上评估了我们的方法，并在我们的任务上实现了显著优于现有基线的性能，同时使用了明显更小的模型。
英文摘要：
Cloth manipulation is a ubiquitous task in everyday life, but it remains an open challenge for robotics. The difficulties in developing cloth manipulation policies are attributed to the high-dimensional state space, complex dynamics, and high propensity to self-occlusion exhibited by fabrics. As analytical methods have not been able to provide robust and general manipulation policies, reinforcement learning (RL) is considered a promising approach to these problems. However, to address the large state space and complex dynamics, data-based methods usually rely on large models and long training times. The resulting computational cost significantly hampers the development and adoption of these methods. Additionally, due to the challenge of robust state estimation, garment manipulation policies often adopt an end-to-end learning approach with workspace images as input. While this approach enables a conceptually straightforward sim-to-real transfer via real-world fine-tuning, it also incurs a significant computational cost by training agents on a highly lossy representation of the environment state. This paper questions this common design choice by exploring an efficient and modular approach to RL for cloth manipulation. We show that, through careful design choices, model size and training time can be significantly reduced when learning in simulation. Furthermore, we demonstrate how the resulting simulation-trained model can be transferred to the real world. We evaluate our approach on the SoftGym benchmark and achieve significant performance improvements over available baselines on our task, while using a substantially smaller model.
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。
