This paper presents a novel model-free method for humanoid-robot quasi-static movement control. Traditional model-based methods often require precise robot model parameters. Additionally, existing learning-based frameworks often train the policy in simulation environments, thereby indirectly relying on a model. In contrast, we propose a proprioceptive framework based only on sensory outputs. It does not require prior knowledge of a robot's kinematic model or inertial parameters. Our method consists of three steps: 1. Planning different pairs of center of pressure (CoP) and foot position objectives within a single cycle. 2. Searching around the current configuration by slightly moving the robot's leg joints back and forth while recording the sensor measurements of its CoP and foot positions. 3. Updating the robot motion with an optimization algorithm until all objectives are achieved. We demonstrate our approach on a NAO humanoid robot platform. Experiment results show that it can successfully generate stable robot motions.

翻译：本文提出一种新颖的无模型方法，用于实现人形机器人准静态运动控制。传统基于模型的方法往往需要精确的机器人模型参数。此外，现有的基于学习的框架通常在仿真环境中训练策略，从而间接依赖模型。相比之下，我们提出一种仅基于传感器输出的本体感觉框架，无需预先了解机器人的运动学模型或惯性参数。该方法包含三个步骤：1. 在单个周期内规划不同的压力中心（CoP）和足部位置目标对；2. 通过前后微动机器人腿部关节，同时记录其CoP和足部位置的传感器测量值，在当前构型附近进行搜索；3. 使用优化算法更新机器人运动，直至所有目标达成。我们在NAO人形机器人平台上验证了该方法。实验结果表明，该方法能够成功生成稳定的机器人运动。