NEW RESEARCH: Your robot can now juggle with you! This project involves @KaiPloeger, @jan_r_peters, @AlapKshirsagar, and others. Called "Catch, Throw, Repeat: Planning for Human-Robot Partner Juggling". it is a real-time planning and control system that lets a robot arm juggle a shared three-ball cascade with a human partner, catching balls the human throws and throwing them back in sync. The hardware is a 4-DoF Barrett WAM arm (500 Hz control) with a 170 mm funnel-shaped gripper (27 degree half-angle) that catches passively, and an eight-camera OptiTrack rig at 125 Hz tracking IR-markered balls. Each ball has its own Kalman filter that switches between a carry-phase random-walk model and a flight-phase ballistic model. A gravity-informed least-squares fit predicts touchdown. A multiple-shooting trajectory optimizer replans in joint space at up to about 20 Hz while the arm is empty, and a per-ball state machine decides which incoming ball to go for. The only prior partner-juggling controller was [Kober 2012], which topped out at about four consecutive robot catches. This approach does zero prediction of the human's intent, tracking only the ball. There is no human pose or intent model anywhere in the loop: the robot picks the ball that has been in flight toward it longest and whose predicted touchdown lands in a reachable workspace. That converts a two-agent coordination problem into a single-object tracking-plus-planning. The 170 mm funnel with a 27 degree half-angle physically corrals the ball, so the optimizer only has to get the funnel to the predicted touchdown at rest (velocity and acceleration both zero), and the paper argues a rest-catch is more robust to timing error than velocity-matching. Continuous roughly 20 Hz replanning happens exclusively in the vacant phase approaching the catch. Once holding a ball the arm commits and executes the release open-loop, deliberately trading feedback for a consistent, repeatable release the human can read. All the adaptivity is spent on catching, none on throwing. Also interesting imho: degradation with ball count is physical, not perceptual, and the scaling wall is geometric. As balls increase, the throw frequency rises, and the vacant-phase slack shrinks. The dominant failure mode is inter-ball collisions, and collision probability "does not admit a closed-form solution," so principled online avoidance is hard and the airspace saturates, capping scalability at two to three balls. Four-ball patterns exist only in simulation, where success also drops sharply.