FlipItRight: Stable Pose-Targeted Throw-Flip Across Diverse Objects
2026-06-01 • Robotics
Robotics
AI summaryⓘ
The authors created FlipItRight, a method for robots with many joints to throw objects so they land in a specific way. They break the task into two parts: deciding how to release the object and planning the robot's motion to do the throw. By carefully choosing when and how to let go, their method handles timing uncertainty better and works well without needing prior training or specific setup. They tested this on different objects and got a 90% success rate. Each part of their design helps improve the throwing accuracy.
high-DoF manipulatorpose-targeted throwobject-level plannerrobot-level plannerrelease stateend-effector velocityswing motionrelease-timing uncertaintyablation studyreal robot experiments
Authors
Axel Dawne, Shinkyu Park
Abstract
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.