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This is a Literature Review study.

September 18, 2025Science Robotics

A review of learning-based dynamics models for robotic manipulation

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Authors

BABo Ai STStephen TianStanford University HSHaochen ShiStanford University

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Implication

This review highlights advances in learning-based models for predictive uncertainty in robotic manipulation, suggesting improvements in control systems.

Key Points

Learning-based dynamics models improve robotic manipulation by capturing complex interactions and predictive uncertainty.
These models demonstrate significant advancements in tasks such as manipulating deformable objects and multiobject interactions.
State representation choices in these models influence the effectiveness of capturing scene dynamics and inductive biases.
Integrating learned dynamics with state estimation enhances robot capabilities and points to critical gaps in current research.

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Ai et al. (2025) studied this question.

synapsesocial.com/papers/68d461d231b076d99fa61467 https://doi.org/10.1126/scirobotics.adt1497

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Implication

This review highlights advances in learning-based models for predictive uncertainty in robotic manipulation, suggesting improvements in control systems.

A review of learning-based dynamics models for robotic manipulation

Discussion

Member takes

Implication

Key Points

Cite This Study

A review of learning-based dynamics models for robotic manipulation

Discussion

Member takes

Implication

Key Points

Cite This Study

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