RT-H: Action Hierarchies Using Language
作者: Suneel Belkhale, Tianli Ding, Ted Xiao, Pierre Sermanet, Quon Vuong, Jonathan Tompson, Yevgen Chebotar, Debidatta Dwibedi, Dorsa Sadigh
分类: cs.RO, cs.AI
发布日期: 2024-03-04 (更新: 2024-06-01)
💡 一句话要点
提出RT-H以解决机器人模仿学习中的任务语义多样性问题
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 机器人模仿学习 语言条件策略 多任务学习 人机协作 动作层次结构 视觉上下文 语言干预
📋 核心要点
- 现有的机器人模仿学习方法在处理语义多样性任务时面临数据共享困难，导致需要大量示范数据。
- 本文提出RT-H方法，通过学习低层动作的语言描述，建立任务与动作之间的桥梁，从而提高学习效率。
- 实验结果表明，RT-H策略在多任务数据集上表现出更好的鲁棒性和灵活性，能够有效响应人类的语言干预。
📝 摘要（中文）
语言为复杂概念提供了可分解的方式。近期的机器人模仿学习研究利用语言条件策略，根据视觉观察和高层次任务预测动作。然而，当任务语义变得更加多样时，任务间的数据共享变得困难，导致学习高层任务与动作之间的映射需要更多的示范数据。为了解决这一问题，本文提出了一种通过语言描述低层动作的方式，帮助机器人学习动作的语言，从而在任务与动作之间架起桥梁。RT-H方法通过学习语言动作的层次结构，增强了策略的鲁棒性和灵活性，能够响应并从人类干预中学习，超越了传统的遥控干预方法。
🔬 方法详解
问题定义：本文旨在解决机器人模仿学习中任务语义多样性带来的数据共享困难，现有方法在处理不同语义任务时需要大量示范数据。
核心思路：通过教会机器人低层动作的语言描述，RT-H方法在任务与动作之间建立了语言层次结构，使得策略能够学习到共享的低层动作结构。
技术框架：RT-H的整体架构包括两个主要阶段：首先学习预测语言动作，然后在此基础上结合高层任务预测具体动作，整个过程充分利用视觉上下文。
关键创新：RT-H的创新在于通过语言动作的层次结构来增强策略的灵活性和鲁棒性，使其能够在多任务环境中有效学习并响应人类干预。
关键设计：在技术细节上，RT-H采用了特定的损失函数来优化语言动作的预测，并设计了适应多任务数据集的网络结构，以确保策略的有效性和适应性。
🖼️ 关键图片
📊 实验亮点
实验结果显示，RT-H策略在多任务数据集上表现出显著的性能提升，相较于传统的遥控干预方法，能够更有效地响应人类的语言干预，提升了学习效率和策略的鲁棒性。
🎯 应用场景
该研究的潜在应用领域包括人机协作、智能家居和服务机器人等。通过增强机器人对语言指令的理解和响应能力，RT-H能够提高机器人在复杂环境中的自主性和灵活性，具有重要的实际价值和未来影响。
📄 摘要（原文）
Language provides a way to break down complex concepts into digestible pieces. Recent works in robot imitation learning use language-conditioned policies that predict actions given visual observations and the high-level task specified in language. These methods leverage the structure of natural language to share data between semantically similar tasks (e.g., "pick coke can" and "pick an apple") in multi-task datasets. However, as tasks become more semantically diverse (e.g., "pick coke can" and "pour cup"), sharing data between tasks becomes harder, so learning to map high-level tasks to actions requires much more demonstration data. To bridge tasks and actions, our insight is to teach the robot the language of actions, describing low-level motions with more fine-grained phrases like "move arm forward". Predicting these language motions as an intermediate step between tasks and actions forces the policy to learn the shared structure of low-level motions across seemingly disparate tasks. Furthermore, a policy that is conditioned on language motions can easily be corrected during execution through human-specified language motions. This enables a new paradigm for flexible policies that can learn from human intervention in language. Our method RT-H builds an action hierarchy using language motions: it first learns to predict language motions, and conditioned on this and the high-level task, it predicts actions, using visual context at all stages. We show that RT-H leverages this language-action hierarchy to learn policies that are more robust and flexible by effectively tapping into multi-task datasets. We show that these policies not only allow for responding to language interventions, but can also learn from such interventions and outperform methods that learn from teleoperated interventions. Our website and videos are found at https://rt-hierarchy.github.io.