Gray-Box Computed Torque Control for Differential-Drive Mobile Robot Tracking
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
This study presents a learning-based nonlinear algorithm for tracking control of differential-drive mobile robots. The Computed Torque Method (CTM) suffers from inaccurate knowledge of system parameters, while Deep Reinforcement Learning (DRL) algorithms are known for sample inefficiency and weak stability guarantees. The proposed method replaces the black-box policy network of a DRL agent with a gray-box Computed Torque Controller (CTC) to improve sample efficiency and ensure closed-loop stability. This approach enables finding an optimal set of controller parameters for an arbitrary reward function using only a few short learning episodes. The Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is used for this purpose. Additionally, some controller parameters are constrained to lie within known value ranges, ensuring the RL agent learns physically plausible values. A technique is also applied to enforce a critically damped closed-loop time response. The controller's performance is evaluated on a differential-drive mobile robot simulated in the MuJoCo physics engine and compared against the raw CTC and a conventional kinematic controller.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 19 references that are in the map. First posted on arXiv 30 Aug 2025.
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4 of 19 references.
References · 4
- 2015Continuous control with deep reinforcement learningcited by 3,407
- 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
- 2018Residual Reinforcement Learning for Robot Controlcited by 274
- 2022Deep Grey-Box Modeling With Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Modelscited by 7
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