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This paper presents a novel model-free method for humanoid-robot quasi-staticmovement control. Traditional model-based methods often require precise robotmodel parameters. Additionally, existing learning-based frameworks often trainthe policy in simulation environments, thereby indirectly relying on a model.In contrast, we propose a proprioceptive framework based only on sensoryoutputs. It does not require prior knowledge of a robot's kinematic model orinertial parameters. Our method consists of three steps: 1. Planning differentpairs of center of pressure (CoP) and foot position objectives within a singlecycle. 2. Searching around the current configuration by slightly moving therobot's leg joints back and forth while recording the sensor measurements ofits CoP and foot positions. 3. Updating the robot motion with an optimizationalgorithm until all objectives are achieved. We demonstrate our approach on aNAO humanoid robot platform. Experiment results show that it can successfullygenerate stable robot motions.

Model-Free and Learning-Free Proprioceptive Humanoid Movement Control