Behavior Cloning of MPC for 3-DOF Robotic Manipulators
While Model Predictive Control (MPC) provides strong stability and robustness, it imposes a significant computational burden on real-time systems. This paper investigates the application of Behavior Cloning to approximate MPC policies for the real-time control of a 3-degree-of-freedom robotic manipulator. We present a baseline controller combining Inverse Kinematics with MPC and evaluate neural network architectures, ranging from classical regression algorithms to deep learning models including Deep MLPs and RNNs, to derive computationally efficient surrogate policies. We analyze generalization capabilities, stability considerations, and the trade-offs inherent in different architectural ...
| Authors | Theo Guegan, Dexter Wen Jie Teo |
|---|---|
| Primary category | cs.RO |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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