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SEEC: Stable End-Effector Control
Humanoid loco-manipulation requires keeping an end-effector stable while the whole body moves. SEEC pairs a model-based controller with residual reinforcement learning so the policy only has to learn what the model gets wrong, rather than re-learning the full task from scratch.
Overview
The model-based component provides a stabilizing prior for end-effector tracking, and a learned residual corrects for unmodeled dynamics during locomotion. The result is a controller that handles loco-manipulation tasks more reliably than either component alone, while remaining sample-efficient enough to train.