外观
论文arXiv 2609.234391 个月前
基于可组合物体中心策略的滚动时域推动
针对任意形状3D物体长时域推动，提出物体中心分层反馈策略，并在仿真与真机上验证。
Franka FR3原文链接：Receding-Horizon Pushing with Composable Object-Centric Policies（arXiv 2609.23439 · 发布日期：2026-09-20）
Receding-Horizon Pushing with Composable Object-Centric Policies
针对任意形状3D物体长时域推动，提出物体中心分层反馈策略，并在仿真与真机上验证。
核心要点
- 长时域推动需同时决定推的位置、单次推动稳定可达性及后续动作可行性。
- 低层从归一化点云预测接触动作，并用准静态滑动与倾覆分析评估稳定性。
- 高层用BIT*搜索物体路径，结合接触预测和机器人运动规划检查未来可行性。
- 仿真中22个物体六种场景明显优于基线，并展示零样本Sim-to-Real真机迁移。
验证范围与局限：验证包括仿真22个物体六种场景、Franka机械臂定量实验和移动操作臂定性演示。
英文摘要 (Abstract)
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.
作者：Zhiyi Yuan, Tao Hu, Anxing Xiao, Yuhong Deng, David Hsu, Hanbo Zhang
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