KinScene：基于模型的关节型场景移动操作
KinScene: Model-Based Mobile Manipulation of Articulated Scenes
AI总结：
KinScene提出一种全栈方法，通过自主探索构建室内场景级关节模型，使移动机械臂能够推理多部件运动并规划顺序交互，从而在真实环境中完成长时程关节型物体操作任务。
摘要：
顺序地与关节型物体交互对于移动机械臂在日常环境中有效作业至关重要。为了实现涉及关节型物体的长时程任务，本研究探索通过自主探索为室内场景构建场景级关节模型。以往研究在考虑物体运动学约束的情况下研究了关节型物体的移动操作，但主要聚焦于单个物体场景，缺乏扩展到场景级上下文以支持任务级规划。为了顺序地操作多个物体部件，机器人需要推理每个部件产生的运动，并预判其对未来动作的影响。我们提出了KinScene，这是一种面向关节型物体长时程操作任务的全栈方法。机器人对场景进行建图，检测关节型物体并与之进行物理交互，收集观测数据，并推断关节属性。对于顺序任务，机器人基于推断出的关节模型规划一系列可行的物体交互。我们证明了该方法能够可重复地构建准确的场景级运动学和几何模型，从而在真实场景中实现长时程移动操作。代码和更多结果可在 https://chengchunhsu.github.io/KinScene/ 获取。
英文摘要：
Sequentially interacting with articulated objects is crucial for a mobile manipulator to operate effectively in everyday environments. To enable long-horizon tasks involving articulated objects, this study explores building scene-level articulation models for indoor scenes through autonomous exploration. While previous research has studied mobile manipulation with articulated objects by considering object kinematic constraints, it primarily focuses on individual-object scenarios and lacks extension to a scene-level context for task-level planning. To manipulate multiple object parts sequentially, the robot needs to reason about the resultant motion of each part and anticipate its impact on future actions. We introduce KinScene, a full-stack approach for long-horizon manipulation tasks with articulated objects. The robot maps the scene, detects and physically interacts with articulated objects, collects observations, and infers the articulation properties. For sequential tasks, the robot plans a feasible series of object interactions based on the inferred articulation model. We demonstrate that our approach repeatably constructs accurate scene-level kinematic and geometric models, enabling long-horizon mobile manipulation in a real-world scene. Code and additional results are available at https://chengchunhsu.github.io/KinScene/
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。
