arXiv AI
Jul 7
Conflict-Based Lazy Search for Fast Multi-Manipulator Planning
arXiv:2607. 04124v1 Announce Type: cross Abstract: Employing multiple manipulators can boost efficiency and accomplish tasks that a single manipulator cannot do.
Zonal RL-RRT: Integrated RL-RRT Path Planning with Collision Probability and Zone Connectivity
Zonal RL-RRT is a new path‑planning algorithm that partitions a map into zones using kd‑tree partitioning and employs Value Iteration as a high‑level decision maker. The method achieves a three‑fold improvement in time efficiency over basic sampling methods such as RRT and RRT* in forest‑like maps, and outperforms heuristic‑guided methods like BIT* and Informed RRT* by 1.5× in runtime while maintaining robust success rates across 2D to 6D environments. It also shows on average a 1.5× better performance than learning‑based methods such as NeuralRRT* and MPNetSMP, and has been validated in simulations of a UR10e arm manipulator in MuJoCo.
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arXiv AI
Aug 10
Scalable Long-Horizon Planning with Staggered Updates for Lifelong MAPF
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 AI
Aug 17
LLM-Advisor: An LLM Advisor for Cost-efficient Path Planning across Multiple Terrains
arXiv:2503. 01236v3 Announce Type: replace-cross Abstract: This paper addresses fixed-graph terrain-aware path refinement, in which a global planner is restricted to a predefined route space and may remain optimal within that space while missing lower-cost terrain corridors available in the native-resolution map.
arXiv AI
Sep 3
DiffuSearch: How Hybrid Trajectory Planning Benefits from Aligned Objectives in Diffusion and Action Space
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arXiv AI
Jul 13
GATS: Graph-Augmented Tree Search with Layered World Models for Efficient Agent Planning
arXiv:2607. 08894v1 Announce Type: new Abstract: Large Language Model (LLM) agents have shown promise in multi-step planning tasks, but existing approaches like LATS (Language Agent Tree Search) and ReAct rely heavily on LLM inference during planning, leading to high computational costs and stochastic behavior.
arXiv AI
Sep 3
What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?
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arXiv AI
Aug 7
Search-Aided Joint Agent-Environment Reinforcement Learning for Robust Lifelong Multi-Agent Path Finding with Rotations
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 AI
Aug 18
Flow Motion Policy: Manipulator Motion Planning with Flow Matching Models
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 AI
Jul 20
Process Reward Informed Tree Rollout for Effective Multi-Turn RL
arXiv:2607. 15610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a key approach for training LLM agents, yet popular methods such as GRPO/RLOO rely on multiple independently sampled complete trajectories for advantage estimation.
arXiv AI
Jul 7
MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation
arXiv:2504. 16738v3 Announce Type: replace-cross Abstract: Planning long-horizon manipulation motions using a set of predefined skills is a central challenge in robotics; solving it efficiently could enable general-purpose robots to tackle novel tasks by flexibly composing generic skills.
arXiv AI
Aug 18
LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset
arXiv:2608. 15175v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making.
arXiv AI
Jul 17
REST: Receding Horizon Explorative Steiner Tree for Zero-Shot Object-Goal Navigation
arXiv:2603. 18624v2 Announce Type: replace-cross Abstract: Zero-shot object-goal navigation (ZSON) requires navigating unknown environments to find a target object without task-specific training.