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Investigating Motor Skill Learning Processes with a Robotic Manipulandum

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Published on: February 12, 2017

A review of learning-based dynamics models for robotic manipulation.

Bo Ai¹, Stephen Tian², Haochen Shi²

¹Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Science Robotics

|September 17, 2025

Summary

This review explores learned dynamics models for robots, which use data to predict physical interactions. These models enhance robot control and simulation, advancing capabilities in complex manipulation tasks.

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Area of Science:

Robotics
Machine Learning
Physics Simulation

Background:

Accurate dynamics models are crucial for robot planning and control.
Physics-based models often require full-state information, which is difficult to obtain in real-world scenarios.
Learned dynamics models offer an alternative by learning from perceived interaction data.

Purpose of the Study:

To provide a comprehensive review of current techniques for learned dynamics models in robotics.
To highlight the trade-offs in designing these models.
To identify research gaps and future directions.

Main Methods:

Review of existing literature on learning-based dynamics models for robotics.
Analysis of state representation techniques and their impact on inductive biases.
Discussion of integration with state estimation and control.

Main Results:

Learned dynamics models capture complex factors, predictive uncertainty, and accelerate simulations.
Advancements in robot capabilities for manipulating deformable objects, granular materials, and multi-object interactions.
The choice of state representation is critical for reduced-order modeling.

Conclusions:

Learned dynamics models are advancing robot manipulation capabilities.
Integration with state estimation and control is key for practical applications.
Further research is needed to address critical gaps in the field.