迈向多样化行为：一个基于人类演示的模仿学习基准
Towards Diverse Behaviors: A Benchmark for Imitation Learning with Human Demonstrations
AI总结：
本文提出D3IL基准环境和数据集，用于评估模仿学习算法学习多模态人类行为的能力，并引入多样性度量指标，通过对现有方法的系统评估为未来算法设计提供参考。
摘要：
利用人类数据进行模仿学习已在教机器人掌握广泛技能方面展现出显著成功。然而，人类行为固有的多样性导致多模态数据分布的出现，从而对现有模仿学习算法构成巨大挑战。量化模型有效捕捉和复现这种多样性的能力仍然是一个开放问题。在这项工作中，我们引入了仿真基准环境以及相应的面向模仿学习的多样化人类演示数据集（D3IL），其明确设计用于评估模型学习多模态行为的能力。我们的环境设计涉及多个需要解决的子任务，考虑对多个物体的操作以增加行为多样性，并且只能由依赖闭环感知反馈的策略来解决。其他可用数据集至少缺少这些具有挑战性的特性之一。为了应对多样性量化的挑战，我们引入了可计算的度量指标，这些指标为模型获取和复现多样化行为的能力提供了有价值的见解。这些度量指标为评估模仿学习算法的鲁棒性和通用性提供了一种实用手段。此外，我们对所提出的任务套件上的最先进方法进行了全面评估。该评估作为评估它们学习多样化行为能力的基准。我们的发现揭示了这些方法在处理捕捉和泛化多模态人类行为这一复杂问题上的有效性，为未来模仿学习算法的设计提供了有价值的参考。
英文摘要：
Imitation learning with human data has demonstrated remarkable success in teaching robots in a wide range of skills. However, the inherent diversity in human behavior leads to the emergence of multi-modal data distributions, thereby presenting a formidable challenge for existing imitation learning algorithms. Quantifying a model's capacity to capture and replicate this diversity effectively is still an open problem. In this work, we introduce simulation benchmark environments and the corresponding Datasets with Diverse human Demonstrations for Imitation Learning (D3IL), designed explicitly to evaluate a model's ability to learn multi-modal behavior. Our environments are designed to involve multiple sub-tasks that need to be solved, consider manipulation of multiple objects which increases the diversity of the behavior and can only be solved by policies that rely on closed loop sensory feedback. Other available datasets are missing at least one of these challenging properties. To address the challenge of diversity quantification, we introduce tractable metrics that provide valuable insights into a model's ability to acquire and reproduce diverse behaviors. These metrics offer a practical means to assess the robustness and versatility of imitation learning algorithms. Furthermore, we conduct a thorough evaluation of state-of-the-art methods on the proposed task suite. This evaluation serves as a benchmark for assessing their capability to learn diverse behaviors. Our findings shed light on the effectiveness of these methods in tackling the intricate problem of capturing and generalizing multi-modal human behaviors, offering a valuable reference for the design of future imitation learning algorithms.
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。
