Core Concepts
Learning sparsity patterns and constraints from data improves prediction accuracy in out-of-distribution scenarios.
Abstract
The article proposes a method to enhance the prediction accuracy of learned robot dynamics models on out-of-distribution states by leveraging sparsity and nonholonomic constraints. It introduces contrastive learning to identify sparsity patterns, learns a distance pseudometric for dimensionality reduction, approximates the constraint manifold, and projects predictions onto learned constraints. The work aims to improve generalization of learned dynamics in robotics.
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Introduction
- Robot autonomy relies on accurate dynamics models.
- Training data often fails to cover out-of-distribution scenarios.
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Key Insights
- Robots exhibit sparse dynamics and nonholonomic constraints.
- Naïvely-trained models may predict non-physical behavior.
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Methodology
- Learning pseudometrics via contrastive learning.
- Sparsifying dynamics input space based on learned patterns.
- Approximating normal space for constraint learning.
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Related Work
- Existing methods focus on improving OOD generalization using symmetry and physical constraints.
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Gaussian Processes
- GPs are used for dynamics learning due to their flexibility.
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Problem Statement
- Unknown dynamics and constraints are learned from available data.
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Evaluation
- The proposed method is evaluated on physical robots showing improved accuracy over baselines.
Stats
We evaluate our approach on a physical differential-drive robot and a simulated quadrotor, showing improved prediction accuracy on OOD data relative to baselines.
Quotes
"Many robots have sparse dynamics, i.e., not all state variables affect the dynamics."
"Enforcing that our learned model conforms to this information can improve accuracy."