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Improving Out-of-Distribution Generalization of Learned Dynamics


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. Introduction Robot autonomy relies on accurate dynamics models. Training data often fails to cover out-of-distribution scenarios. Key Insights Robots exhibit sparse dynamics and nonholonomic constraints. Naïvely-trained models may predict non-physical behavior. Methodology Learning pseudometrics via contrastive learning. Sparsifying dynamics input space based on learned patterns. Approximating normal space for constraint learning. Related Work Existing methods focus on improving OOD generalization using symmetry and physical constraints. Gaussian Processes GPs are used for dynamics learning due to their flexibility. Problem Statement Unknown dynamics and constraints are learned from available data. 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."

Deeper Inquiries

How can the proposed method be extended to more complex robotic systems

To extend the proposed method to more complex robotic systems, several modifications and enhancements can be considered. One approach could involve incorporating additional layers of abstraction in the dynamics learning process to capture higher-order interactions and complexities present in the system. This may entail using more sophisticated function approximators such as deep neural networks (DNNs) or recurrent neural networks (RNNs) to model intricate relationships between states and controls. Additionally, introducing hierarchical learning frameworks that allow for multi-level representations of dynamics could help handle the intricacies of complex robotic systems. Furthermore, integrating reinforcement learning techniques for adaptive control policies based on learned dynamics models can enhance adaptability and performance in dynamic environments.

What are the potential limitations of relying solely on learned constraints for improving prediction accuracy

While relying solely on learned constraints can offer significant benefits in improving prediction accuracy for robot dynamics models, there are potential limitations to consider. One key limitation is the generalization capability of learned constraints across diverse operating conditions or unforeseen scenarios. If the constraints are overfitted to specific training data or fail to capture all possible variations in system behavior, there is a risk of reduced accuracy when encountering out-of-distribution inputs. Moreover, inaccuracies or noise in constraint estimation from limited data samples could lead to suboptimal predictions and potentially compromise overall system performance.

How might hardware imperfections impact the effectiveness of the proposed approach

Hardware imperfections can have a notable impact on the effectiveness of the proposed approach for improving prediction accuracy in robot dynamics modeling. Variations or discrepancies between simulated models used for training and real-world hardware implementations may introduce uncertainties that affect constraint identification and sparsity pattern recognition. Inaccuracies stemming from sensor noise, actuator delays, calibration errors, or mechanical wear-and-tear could distort data collected during operation, leading to incorrect assumptions about system constraints. These discrepancies might result in suboptimal projections onto learned constraint manifolds during prediction tasks, ultimately affecting model reliability and robustness against OOD inputs.
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