arXiv:2607. 00444v1 Announce Type: cross Abstract: Spatiotemporal motion planning, especially in multi-robot settings, requires robots to reason about collision-free regions that change over time, which is challenging in continuous spaces when feasible regions are transient and geometrically constrained.
arXiv:2607. 28679v1 Announce Type: new Abstract: Multi-agent planning problems arise in a variety of engineering applications, such as multi-robot wildfire fighting and unmanned aerial inspection in factories.
arXiv:2607. 04124v1 Announce Type: cross Abstract: Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do.
arXiv:2608. 06702v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires generating collision-free paths for large agent fleets under strict real-time constraints.
arXiv:2607. 19306v1 Announce Type: cross Abstract: Autonomous flight in cluttered environments requires a robot to build a geometric map of its surroundings and plan safe, dynamically feasible trajectories, all onboard and in real time.
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
arXiv:2608. 05588v1 Announce Type: cross Abstract: Lifelong Multi-Agent Path Finding (LMAPF) requires repeatedly planning collision-free paths for agents that continuously receive new goals upon reaching their current ones.
arXiv:2608. 07734v1 Announce Type: cross Abstract: Automated warehouses face a fundamental trade-off between maximizing storage density and achieving high retrieval throughput.
arXiv:2604. 07084v2 Announce Type: replace-cross Abstract: Open-loop end-to-end neural motion planners have recently been proposed to improve motion planning for robotic manipulators.
arXiv:2504. 17901v3 Announce Type: replace-cross Abstract: Task and motion planning (TAMP) is a well-established approach for solving long-horizon robot planning problems.
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
arXiv:2510. 09204v4 Announce Type: replace-cross Abstract: Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces.
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.