面向开放世界机器人操作的高效感知运动学习
Efficient Sensorimotor Learning for Open-world Robot Manipulation
AI总结：
本论文面向开放世界机器人操作，提出以规律性为核心的高效感知运动学习方法，通过物体中心先验、空间理解和可复用技能识别，使机器人从少量示范或视频中快速学习并泛化多任务操作技能。
摘要：
本论文研究开放世界机器人操作问题，即机器人必须对未经预编程或预训练的新物体、场景或任务进行泛化或快速适应。本论文采用高效感知运动学习方法来解决该问题。实现高效感知运动学习的关键在于利用有限示范数据中存在的规律性模式。这些被称为“规律性”的模式使得可泛化操作技能的数据高效学习成为可能。本论文通过规律性的视角为操作问题的形式化提供了新的视角。基于这一概念，我们提出了三项主要贡献。第一，我们提出了赋予机器人以物体为中心先验的方法，使其能够从少量遥操作示范中学习可泛化的闭环感知运动策略。第二，我们提出了构建机器人空间理解的方法，解锁其从自然场景视频观测中模仿操作技能的能力。最后但同样重要的是，我们提出了使机器人能够从过去经验中识别可复用技能的方法，从而形成能够以顺序方式持续模仿多个任务的系统。总而言之，本论文的贡献有助于为构建通用个人机器人奠定基础，这些机器人能够以低成本的数据收集快速适应新情况或新任务，并易于与人类交互。通过使机器人能够从有限数据中学习和泛化，本论文朝着实现可无缝融入日常场景的智能机器人助手这一愿景迈出了一步。
英文摘要：
This dissertation considers Open-world Robot Manipulation, a manipulation problem where a robot must generalize or quickly adapt to new objects, scenes, or tasks for which it has not been pre-programmed or pre-trained. This dissertation tackles the problem using a methodology of efficient sensorimotor learning. The key to enabling efficient sensorimotor learning lies in leveraging regular patterns that exist in limited amounts of demonstration data. These patterns, referred to as ``regularity,'' enable the data-efficient learning of generalizable manipulation skills. This dissertation offers a new perspective on formulating manipulation problems through the lens of regularity. Building upon this notion, we introduce three major contributions. First, we introduce methods that endow robots with object-centric priors, allowing them to learn generalizable, closed-loop sensorimotor policies from a small number of teleoperation demonstrations. Second, we introduce methods that constitute robots' spatial understanding, unlocking their ability to imitate manipulation skills from in-the-wild video observations. Last but not least, we introduce methods that enable robots to identify reusable skills from their past experiences, resulting in systems that can continually imitate multiple tasks in a sequential manner. Altogether, the contributions of this dissertation help lay the groundwork for building general-purpose personal robots that can quickly adapt to new situations or tasks with low-cost data collection and interact easily with humans. By enabling robots to learn and generalize from limited data, this dissertation takes a step toward realizing the vision of intelligent robotic assistants that can be seamlessly integrated into everyday scenarios.
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。
