HOLO-MPPI: Multi-Scenario Motion Planning via Hierarchical Policy Optimization
Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning. End-to-end reinforcement learning (RL) can generalize across scenarios but often becomes brittle under distribution shift, reward misspecification, and stochastic interactions. Model predictive path integral (MPPI) control enables strong real-time refinement without gradients, but its performance depends on a well-shaped sampling prior, while manually designing the priors does not scale to multi-scenario deployment. We present HOLO-MPPI (High-level Offline, Low-level Online MPPI), a multi-scenario motion planning framework that combines high-level policy learning with low-level stoc...
| Authors | Youngjae Min, Jovin D'sa, Faizan M. Tariq et al. |
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| Primary category | cs.RO |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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