STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching

• URL: http://arxiv.org/abs/2603.08478v2
• Date: Sun, 15 Mar 2026 17:35:08 GMT
• Title: STRIDE: Structured Lagrangian and Stochastic Residual Dynamics via Flow Matching
• Abstract summary: We propose STRIDE, a dynamics learning framework that separates conservative rigid-body mechanics from uncertain, effectively non-conservative interaction effects.<n>We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1ruped, and the Unitree G1 humanoid.<n>Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines.
• Score: 2.5845893156827158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic systems operating in unstructured environments must operate under significant uncertainty arising from intermittent contacts, frictional variability, and unmodeled compliance. While recent model-free approaches have demonstrated impressive performance, many deployment settings still require predictive models that support planning, constraint handling, and online adaptation. Analytical rigid-body models provide strong physical structure but often fail to capture complex interaction effects, whereas purely data-driven models may violate physical consistency, exhibit data bias, and accumulate long-horizon drift. In this work, we propose STRIDE, a dynamics learning framework that explicitly separates conservative rigid-body mechanics from uncertain, effectively stochastic non-conservative interaction effects. The structured component is modeled using a Lagrangian Neural Network (LNN) to preserve energy-consistent inertial dynamics, while residual interaction forces are represented using Conditional Flow Matching (CFM) to capture multi-modal interaction phenomena. The two components are trained jointly end-to-end, enabling the model to retain physical structure while representing complex stochastic behavior. We evaluate STRIDE on systems of increasing complexity, including a pendulum, the Unitree Go1 quadruped, and the Unitree G1 humanoid. Results show 20% reduction in long-horizon prediction error and 30% reduction in contact force prediction error compared to deterministic residual baselines, supporting more reliable model-based control in uncertain robotic environments.

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