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Related Experiment Video

Updated: Jul 3, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Random sampling of states in dynamic programming.

Christopher G Atkeson¹, Benjamin J Stephens

¹Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|July 18, 2008

Summary

This summary is machine-generated.

This study introduces a novel approach to approximate dynamic programming for robotics control problems. It focuses on finding steady-state policies using sparse sampling and local optimization for improved performance.

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Last Updated: Jul 3, 2026

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion

Published on: March 1, 2022

Area of Science:

Robotics
Artificial Intelligence
Control Theory

Background:

Approximate dynamic programming (ADP) is crucial for complex control problems.
Existing ADP methods face challenges with continuous state and action spaces.
Robotics applications often require efficient steady-state policy determination.

Purpose of the Study:

To develop an integrated ADP framework for deterministic, time-invariant, discrete-time control problems.
To address challenges in continuous state and action spaces common in robotics.
To find effective steady-state policies for robotic systems.

Main Methods:

Combining sparse random sampling of states.
Employing value function and policy approximation using local models.
Utilizing local trajectory optimizers for global policy optimization.

Main Results:

Demonstrated initial success on simulated robotics problems.
The integrated approach shows promise for finding steady-state policies.
Validation of the combined ADP techniques in continuous control scenarios.

Conclusions:

The proposed integrated ADP approach is effective for robotics control.
Sparse sampling and local optimization enhance policy and value function approximation.
This method provides a viable path towards solving complex robotic control tasks.