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Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

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Published on: April 11, 2018

DOOMED: Direct Online Optimization of Modeling Errors in Dynamics.

Nathan Ratliff¹, Franziska Meier^1,2,3, Daniel Kappler^1,2

¹1 Lula Robotics, Inc. , Seattle, Washington.

|December 20, 2016

Summary

This summary is machine-generated.

This study introduces Direct Online Optimization of Modeling Errors in Dynamics (DOOMED) to improve robot control. DOOMED directly corrects inverse dynamics errors in real-time for better tracking performance.

Keywords:

adaptive control feedback control inverse dynamics learning control manipulation online learning

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Area of Science:

Robotics
Control Theory
Machine Learning

Background:

Model-based control aims to enhance tracking performance and compliance.
Accurate inverse dynamics models are crucial for model-based control.
Existing methods struggle with off-distribution training data when initial tracking errors are significant.

Purpose of the Study:

To develop a novel adaptive control approach for real-time correction of inverse dynamics errors.
To improve tracking accuracy in robotic systems.
To address limitations of current learning-based inverse dynamics models.

Main Methods:

Introduced Direct Online Optimization of Modeling Errors in Dynamics (DOOMED), a class of gradient-based online learning algorithms.
Defined an objective function to minimize the divergence between actual and desired accelerations.
Demonstrated that the gradient of the objective is observable online from system data.

Main Results:

Developed a novel adaptive control approach based on online learning.
Successfully corrected inverse dynamics errors in real-time.
Achieved accurate desired accelerations during robot execution using streaming data.

Conclusions:

DOOMED offers a direct and effective method for real-time inverse dynamics error correction.
This approach enhances robotic tracking performance by overcoming limitations of traditional methods.
The proposed adaptive control strategy enables robots to achieve desired accelerations accurately.