Task-Error Residual Learning for Real-Robot Five-Ball Juggling
Residual learning methods were tested for real-robot five-ball juggling using directional task-error supervision and model-driven exploration. The system achieved stable juggling patterns by refining existing behavior from a simple stack, showing that sample efficiency depends on how information is used. This suggests a path to bridge the sim-to-real gap for dynamic tasks.