机器人自主建模可动物体场景，实现长时间复杂操作
KinScene: Model-Based Mobile Manipulation of Articulated Scenes
- 通过自主探索构建场景级运动模型
- 在真实环境中重复生成高精度运动与几何模型
- 适合长期任务规划的移动操作研究者
连续操作可动物体对移动机械臂在日常环境中的有效运作至关重要。为支持涉及可动物体的长时程任务，本研究通过自主探索构建室内场景的场景级运动模型。以往研究虽考虑了物体运动约束，但主要局限于单个物体场景，缺乏对任务级规划的场景级扩展。为顺序操作多个物体部件，机器人需推理各部件的运动结果，并预判其对后续动作的影响。我们提出KinScene，一种端到端的长时程可动物体操作方法：机器人测绘场景、检测并物理交互可动物体、收集观测数据并推断其运动属性；针对序列任务，基于推断出的运动模型规划可行的交互序列。实验表明，该方法能可靠构建精确的场景级运动与几何模型，实现在真实场景中的长时程移动操作。代码与附加结果见https://chengchunhsu.github.io/KinScene/
原文摘要 · Abstract (English)
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/
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