Optimal Control with Learned Critics under Unmodeled State Dependencies
Model Predictive Control (MPC) provides a structured and constraint-aware mechanism for decision-making, but its reliance on optimization-friendly analytical dynamics models limits its use in tasks with contacts and other hard-to-model state dependencies. Model-free reinforcement learning avoids explicit modeling assumptions but typically requires large amounts of interaction data. We present a learning-based MPC framework that combines the data efficiency and structure of local model-based planning with learned components that compensate for incomplete dynamics and finite-horizon myopia. The method augments a nominal analytical model with a residual dynamics network that learns missing s...
| Authors | Philipp Schoch, Markus Ryll |
|---|---|
| Primary category | cs.RO |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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