Seated Walk by a Musculoskeletal Humanoid with Buttock-Contact Sensors Using Constrained Teaching

Centrala begrepp

A musculoskeletal humanoid robot with buttock-contact sensors can perform seated walk by using a constrained teaching method that learns only the transition condition thresholds.

Sammanfattning

This study presents the realization of seated walk, a movement of walking while sitting on a chair with casters, on a musculoskeletal humanoid robot called MusashiOLegs. The key aspects are:

1. Implementation of buttock-contact sensors on the planar interskeletal structure of the robot's body to measure the contact force between the robot and the chair. This enables balance control during seated walk.

2. Development of a constrained teaching method (CTM) where the robot's motion is described by one-dimensional control commands, their transitions, and transition conditions. Only the threshold values of the transition conditions are learned from human teaching, simplifying the teaching process.

3. Experiments demonstrating the robot's ability to perform forward, backward, and rotational movements during seated walk by combining the taught motions. The robot can execute the reproduced motions at a faster speed compared to the teaching.

4. Evaluation of the importance of the buttock-contact balance control, which is crucial for translational movements but less significant for rotational movements.

5. Demonstration of the robot carrying an object by integrating the seated walk motions, showcasing the potential for practical applications.

The key contribution is the realization of seated walk, an underexplored locomotion mode, on a musculoskeletal humanoid robot using a novel teaching method and balance control approach.

Statistik

Flhip - Frhip = 14800 N when sitting properly
Flhip - Frhip = 11800 N when sitting with weight on the left
Flhip - Frhip = 17600 N when sitting with weight on the right

Citat

"By learning only the threshold of the transition condition, each motion was successfully performed."
"The motion durations of teaching and reproduction were 47 [sec] →17 [sec] for Move-Forward, 42 [sec] →16 [sec] for Move-Backward, and 63 [sec] →17 [sec] for Rotate-Left."

Viktiga insikter från

Realization of Seated Walk by a Musculoskeletal Humanoid with Buttock-Contact Sensors From Human Constrained Teaching

by Kento Kawaha... på arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00892.pdf

Djupare frågor

How can the constrained teaching method be extended to handle more dynamic motions beyond quasi-static states

To extend the constrained teaching method to handle more dynamic motions beyond quasi-static states, several enhancements can be implemented. One approach could involve incorporating predictive modeling techniques to anticipate the dynamic behavior of the musculoskeletal humanoid based on the learned threshold values. By integrating predictive algorithms, the system can adjust control commands in real-time to account for dynamic changes in the environment or the robot's state. Additionally, introducing feedback mechanisms that continuously monitor the robot's stability and performance can help in dynamically adjusting the control commands to ensure smooth and stable motion execution. Furthermore, integrating machine learning algorithms that can adapt and optimize the control strategies based on real-time sensor feedback can enhance the system's capability to handle dynamic motions effectively.

What are the potential challenges in parameterizing the taught seated walk motions to enable adjustable movement magnitudes

Parameterizing the taught seated walk motions to enable adjustable movement magnitudes poses several potential challenges. One key challenge is determining the appropriate parameters that can effectively scale the magnitude of each motion component while maintaining the overall integrity of the movement. This requires a thorough understanding of the interdependencies between different motion parameters and how they contribute to the overall motion. Additionally, ensuring that the parameterization process does not introduce instability or inconsistency in the robot's movements is crucial. Calibration and validation procedures need to be established to fine-tune the parameters and verify their impact on the robot's performance. Moreover, developing a user-friendly interface that allows operators to easily adjust the parameters and visualize the resulting changes in motion magnitude is essential for practical implementation.

How could the seated walk capabilities be integrated with upper-body manipulation to enable more complex task execution on the musculoskeletal humanoid

Integrating the seated walk capabilities with upper-body manipulation can significantly enhance the musculoskeletal humanoid's task execution capabilities. By combining the learned seated walk motions with upper-body manipulation skills, the robot can perform complex tasks that involve both locomotion and manipulation. For example, the robot can pick up objects while moving in a seated position, navigate through cluttered environments, or interact with objects at different heights. This integration requires developing coordinated control strategies that synchronize the movements of the upper body with the seated walk motions. Additionally, implementing sensory feedback mechanisms that enable the robot to adapt its movements based on the environment and task requirements is essential for successful task execution. By seamlessly blending seated walk capabilities with upper-body manipulation, the musculoskeletal humanoid can achieve a higher level of versatility and autonomy in performing various tasks.