FlipItRight: Stable Pose-Targeted Throw-Flip Across Diverse Objects
Enables robots to perform precise throw-flip manipulation on novel objects and targets without calibration or data collection, addressing a key challenge in dynamic manipulation.
FlipItRight achieves 90% success rate across 120 trials for planar pose-targeted throw-flip of diverse objects using a high-DoF manipulator, without requiring prior data or learned models.
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.