让机械臂稳定抛翻不同物体，精准落地朝向。
FlipItRight: Stable Pose-Targeted Throw-Flip Across Diverse Objects
- 把释放状态作为中间变量，优化抛掷动作设计。
- 真实场景测试120次成功率达90%。
- 无需训练数据，可直接用于新物体和目标。
我们提出FlipItRight，一种基于高自由度机械臂的稳定平面姿态目标抛翻框架。任务分解为物体级规划器（生成满足目标落地姿态的候选释放状态）和机器人级规划器（评估可执行性并构建可行摆动轨迹）。将释放状态作为显式中间表示，实现合理的候选筛选、释放与预摆位配置的自适应选择，以及近释放阶段的结构化运动设计——特别是最终摆动阶段保持末端执行器速度近似恒定，以提升对释放时机不确定性的鲁棒性。在真实平台对形状、尺寸和质量各异的物体进行验证，120次试验中成功率达90%。消融实验表明每一项设计均有助于提升投掷性能，且该框架无需先验数据或学习模型，可在无环境特异性校准或数据采集的情况下直接部署于新物体与目标。
原文摘要 · Abstract (English)
We propose FlipItRight, a framework for stable planar pose-targeted throw-flip with a high-DoF manipulator. The task is decomposed into an object-level planner, which generates candidate release states satisfying the desired landing pose, and a robot-level planner, which evaluates executability and constructs a feasible swing motion. Treating the release state as an explicit intermediate representation enables principled candidate filtering, adaptive selection of release and pre-swing configurations, and structured near-release motion design -- in particular, approximately constant end-effector velocities during the final swing phase to improve robustness to release-timing uncertainty. We validate on a real platform across objects of varying shape, size, and mass, achieving a 90% success rate across 120 trials. Ablation studies confirm that each design choice contributes to throwing performance, and the framework requires no prior data or learned model, enabling direct deployment on new objects and targets without environment-specific calibration or data collection.
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