arXiv:2606.16480v1 Announce Type: cross Abstract: 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 (
The increasing deployment of robots in diverse real-world scenarios necessitates more robust and adaptable motion planning solutions, making the brittleness of current methods a critical focus.
This research addresses a core challenge in robotics: enabling generalizable, reliable motion planning across varied environments without extensive manual tuning, which is crucial for scalable autonomy.
The development of more adaptable and robust motion planning algorithms like HOLO-MPPI could accelerate the practical deployment of advanced robotics beyond structured environments.
- · Robotics manufacturers
- · Logistics and industrial automation
- · AI researchers
- · Companies relying on highly specialized, single-scenario robotic solutions
- · Traditional motion planning approaches
Improved performance and broader application of robots in dynamic and complex real-world settings.
Reduced operational costs and increased efficiency in industries adopting advanced robotic automation.
Acceleration of general-purpose robot development, potentially leading to more widespread adoption across various economic sectors.
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Read at arXiv cs.AI