PlayFusion：通过从语言标注的玩耍数据进行扩散来习得技能
PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play
AI总结：
提出 PlayFusion，用语言标注的非结构化玩耍数据训练多任务条件扩散模型，并通过离散瓶颈提取可复用的机器人技能词汇。
摘要：
从非结构化且未经筛选的数据中学习，已成为语言和视觉领域生成式方法的主导范式。这类非结构化且无引导的行为数据通常被称为玩耍（play），在机器人学中也更容易采集，但由于其本身具有多模态、含噪和次优的特性，要从中学习却困难得多。本文研究如何从事后用语言标注的非结构化玩耍数据中学习目标导向的技能策略这一问题。具体而言，我们利用扩散模型的进展，学习一个多任务扩散模型，以从玩耍数据中提取机器人技能。通过在状态和动作空间中使用条件去噪扩散过程，我们能够妥善处理玩耍数据的复杂性和多模态性，并生成多样且有趣的机器人行为。为了使扩散模型更适用于技能学习，我们通过在条件行为生成过程中引入离散瓶颈，鼓励机器人智能体习得一套技能词汇。在实验中，我们在仿真和真实世界的多种环境中证明了本方法的有效性。结果可视化和视频见 https://play-fusion.github.io。
英文摘要：
Learning from unstructured and uncurated data has become the dominant paradigm for generative approaches in language and vision. Such unstructured and unguided behavior data, commonly known as play, is also easier to collect in robotics but much more difficult to learn from due to its inherently multimodal, noisy, and suboptimal nature. In this paper, we study this problem of learning goal-directed skill policies from unstructured play data which is labeled with language in hindsight. Specifically, we leverage advances in diffusion models to learn a multi-task diffusion model to extract robotic skills from play data. Using a conditional denoising diffusion process in the space of states and actions, we can gracefully handle the complexity and multimodality of play data and generate diverse and interesting robot behaviors. To make diffusion models more useful for skill learning, we encourage robotic agents to acquire a vocabulary of skills by introducing discrete bottlenecks into the conditional behavior generation process. In our experiments, we demonstrate the effectiveness of our approach across a wide variety of environments in both simulation and the real world. Results visualizations and videos at https://play-fusion.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。
