# Model-Free and Learning-Free Proprioceptive Humanoid Movement Control

## TL;DR

This paper introduces a model-free, learning-free proprioceptive control method for humanoid robots that relies solely on sensory outputs, enabling stable quasi-static movements without prior model knowledge.

## Contribution

It presents a novel proprioceptive framework that does not require robot models or simulation training, using an optimization-based approach for stable humanoid movement control.

## Key findings

- Successfully generates stable motions on NAO robot
- Operates without prior kinematic or inertial models
- Demonstrates effectiveness in real-world experiments

## Abstract

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.