Physics-Informed Policy Optimization via Analytic Dynamics Regularization
Reinforcement learning (RL) has achieved strong performance in robotic control; however, state-of-the-art policy learning methods, such as actor-critic methods, still suffer from high sample complexity and often produce physically inconsistent actions. This limitation stems from neural policies implicitly rediscovering complex physics from data alone, despite accurate dynamics models being readily available in simulators. In this paper, we introduce a novel physics-informed RL framework, called PIPER, that seamlessly integrates physical constraints directly into neural policy optimization with analytical soft physics constraints. At the core of our method is the integration of a different...
| Authors | Namai Chandra, Liu Mohan, Zhihao Gu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 March 2026 |
| Last updated at source | 21 March 2026 |
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