Data-Driven Physics Embedded Dynamics with Predictive Control and Reinforcement Learning for Quadrupeds
State of the art quadrupedal locomotion approaches integrate Model Predictive Control (MPC) with Reinforcement Learning (RL), enabling complex motion capabilities with planning and terrain adaptive behaviors. However, they often face compounding errors over long horizons and have limited interpretability due to the absence of physical inductive biases. We address these issues by integrating Lagrangian Neural Networks (LNNs) into an RL MPC framework, enabling physically consistent dynamics learning. At deployment, our inverse dynamics infinite horizon MPC scheme avoids costly matrix inversions, improving computational efficiency by up to 4x with minimal loss of task performance. We validat...
| Authors | Prakrut Kotecha, Aditya Shirwatkar, Shishir Kolathaya |
|---|---|
| Primary category | cs.RO |
| Published | 15 March 2026 |
| Last updated at source | 15 March 2026 |
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