滚动时域推挤：基于可组合对象中心策略

中文摘要 AI 辅助

非抓取式操作对于重新定位大型、重型或几何上无法抓取的对象非常实用。然而，对任意形状的3D对象进行长时域推挤，需要耦合三个问题：1）在哪里推挤对象以接近目标位姿；2）每次推挤是否稳定且可达；3）后续动作是否仍然可行。我们提出了一种在反馈引导的分层框架内的对象中心推挤策略。在低层，一种基于学习的策略从位姿和尺度归一化的点云中预测接触动作，并以近单步子目标为条件。应用稳定性评分，通过准静态滑动与倾倒分析来评估预测的接触。在高层，BIT*首先搜索对象路径，并通过接触预测和机器人运动规划检查接下来的几个子目标的未来可行性。失败的运动规划作为反馈，改变局部路径成本并触发重新规划。在执行过程中，仅执行第一个可行动作。在仿真中，我们评估了六种不同场景中的22个对象，并在此基础上进行了全面的消融研究。结果表明，我们的方法以明显优势超越基线，并能在不同情况下可靠地完成长时域对象推挤任务。我们还报告了使用Franka机械臂的定量真实机器人实验，以及使用移动机械臂对大型和重型对象的定性演示，实现了直接的零样本仿真到现实迁移。

英文摘要

Non-prehensile manipulation is practical for relocating large, heavy, or geometrically ungraspable objects. Yet, long-horizon pushing of arbitrarily-shaped 3D objects couples three problems: 1) where to push the object so as to approach the target pose, 2) whether each push is stable and reachable, 3) whether subsequent actions remain feasible. We present an object-centric pushing policy within a feedback-guided hierarchical framework. At the low level, a learning-based policy predicts contact actions from a pose- and scale-normalized point cloud, conditioned on a near single-step subgoal. A stability score is applied to evaluate the predicted contacts by a quasi-static sliding-versus-tipping analysis. At the high level, BIT$^*$ first searches for an object path, and the next several subgoals are checked by contact prediction and robot motion planning for future feasibility. Failed motion plans, as feedback, change the local path costs and trigger re-planning. During execution, only the first feasible action is executed. In simulation, we evaluate 22 objects in six different scenes, upon which we also conduct comprehensive ablation studies. Results demonstrate that our method outperforms baselines with a clear margin and can reliably achieve long-horizon object pushing tasks under different situations. We also report quantitative real-robot experiments with a Franka arm and qualitative demonstrations with a mobile manipulator for large and heavy objects, with directly zero-shot sim-to-real transfer.

发表机构

• National University of Singapore（新加坡国立大学）
• Shanghai Innovation Institute（上海创新研究院）

机构由 AI 辅助整理，请以论文原文为准。