Reward‑Guided RRT* is a sampling‑based framework that builds budget‑feasible robotic missions by iteratively selecting rewarding locations and planning motions directly in continuous state space, without a pre‑computed task‑transition graph. The method uses reward‑guided RRT* searches and a return‑feasibility check to ensure each task can be visited within the travel budget. Experiments on increasingly complex obstacle environments and task counts show solutions comparable to VNS‑PRM* while sampling far fewer states, and the framework is extended to Dubins steering and validated on a scaled car‑like robot in CoppeliaSim.
By Vin\'icius Ara\'ujo de Oliveira, Gabriel Ferreira Mendes Louren\c{c}o, Paulo L. J. Drews-Jr, Armando Alves Neto, Douglas G. Macharet
arXiv:2608.29315v1 Announce Type: cross
Abstract: This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic...
By Christopher Tatsch, Yu Gu
arXiv:2604. 12474v3 Announce Type: replace-cross Abstract: In many robotic tasks, agents must traverse a sequence of spatial regions to complete a mission.
By Lidor Erez, Shahaf S. Shperberg, Ayal Taitler
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.
By Jingtao Tang, Zining Mao, Lufan Yang, Hang Ma
arXiv:2608.24485v1 Announce Type: cross
Abstract: Automated parking commonly assumes marked slots and short approach maneuvers. Delivery and service vehicles, however, may need to reach an operator-s...
By Zihan Wang, Bai Huang, Yang Guan, Xiao Li, Haoyu Xu, Naizheng Wang, Shengbo Eben Li
arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.
By Apoorva Vashisth (Purdue University), Manav Kulshrestha (Purdue University), Pranav Bakshi (IIT Kharagpur), Damon Conover (DEVCOM Army Research Lab), Guillaume Sartoretti (National University of Singapore), Aniket Bera (Purdue University)
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.
By Benned Hedegaard, Yichen Wei, Ziyi Yang, Ahmed Jaafar, Stefanie Tellex, George Konidaris, Naman Shah
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.
By Davood Soleymanzadeh, Xiao Liang, Minghui Zheng
arXiv:2608. 14466v1 Announce Type: cross Abstract: An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes.
By Ajith Anil Meera, Pablo Lanillos, Wouter Kouw
arXiv:2604. 16509v2 Announce Type: replace-cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning.
By Adithya V. Sastry, Bibek Poudel, Weizi Li
arXiv:2606. 16480v1 Announce Type: cross Abstract: Robots deployed in the real world must plan motions across diverse scenarios without per-scenario retuning.
By Youngjae Min, Jovin D'sa, Faizan M. Tariq, David Isele, Navid Azizan, Sangjae Bae
The paper introduces QDTraj, a method that uses Quality‑Diversity algorithms to automatically generate a diverse set of low‑level trajectory primitives for manipulating articulated objects. By leveraging sparse reward exploration, QDTraj produces at least five times more diverse trajectories for hinge and slider tasks compared to baseline methods, and demonstrates strong generalization across 30 articulations from the PartNetMobility dataset, averaging 704 trajectories per task. The resulting primitives are validated both in simulation and on real robots, with the code released publicly.
By Mathilde Kappel, Mahdi Khoramshahi, Louis Annabi, Faiz Ben Amar, St\'ephane Doncieux