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This research bridges the gap between structured Model Predictive Control and data-hungry reinforcement learning by using learned critics to handle unmodeled state dependencies like physical contacts. By integrating a residual dynamics network with a GPU-accelerated solver, the framework achieves scalable, efficient control in robotics simulators without sacrificing constraint awareness. The work represents a significant step forward for model-based RL in complex environments where analytical models alone are insufficient.
Optimal Control with Learned Critics under Unmodeled State Dependencies
arXiv:2610.05359v1 Announce Type: cross Abstract: 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 state-dependent effects from data and combines the resulting planner with a learned action-value critic that injects long-horizon MDP structure into the local iLQR optimization. To make this practical at reinforcement-learning scale, we develop a GPU-accelerated batched iLQR solver that evaluates learned dynamics and critic networks inside the optimal-control loop and solves thousands of trajectory-optimization problems in parallel. The complete system is integrated into a robotics simulator, enabling scalable model-based reinforcement learning under incomplete dynamics. Experiments on biased and incompletely modeled control tasks show that the approach improves closed-loop control performance while preserving the model-based structure needed for efficient constrained trajectory optimization.
Abstract: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 state-dependent effects from data and combines the resulting planner with a learned action-value critic that injects long-horizon MDP structure into the local iLQR optimization. To make this practical at reinforcement-learning scale, we develop a GPU-accelerated batched iLQR solver that evaluates learned dynamics and critic networks inside the optimal-control loop and solves thousands of trajectory-optimization problems in parallel. The complete system is integrated into a robotics simulator, enabling scalable model-based reinforcement learning under incomplete dynamics. Experiments on biased and incompletely modeled control tasks show that the approach improves closed-loop control performance while preserving the model-based structure needed for efficient constrained trajectory optimization.
Keywords: Model Predictive Control, Model-Based Reinforcement Learning, iLQR, Value Function Approximation. Presented at 10th Conference on Robot Learning (CoRL 2026), Austin TX, USA
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2610.05359 [cs.RO]
(or arXiv:2610.05359v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.05359 arXiv-issued DOI via DataCite (pending registration)
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