The paper presents DORA, an online learning algorithm for robot navigation that uses Dijkstra’s algorithm as an exact planning engine under a weaker condition than usual causality—specifically, nonnegativity of a reduced cost on a determinized map. DORA calls a shortest‑path oracle a fixed number of times per episode, avoids estimating transition kernels, and incorporates a logarithmic survival weight to keep contact probabilities with dynamic obstacles within a budget. Experiments on grid‑world, directional drilling, and drone surveillance benchmarks show that DORA matches optimistic value iteration with the true transition kernel while performing 4.5 to 19.3 times less planner work, reduces contacts by a factor of seventeen compared to determinize‑and‑replan, and maintains contact rates within wide budget ranges.
Mobile robots that operate in side by side with humans and critical facilities must reach their goals at low cost, despite often unknown true traversal costs of the map apriori and imperfect actuation...
arXiv:2508. 19186v2 Announce Type: replace-cross Abstract: Reactive obstacle avoidance methods often cause agents to become trapped in local minima, because they can often only reason one step ahead (i.
arXiv:2606. 06618v1 Announce Type: cross Abstract: How can we plan long-horizon routes that reach designated goals, visit required waypoints, and remain short when only short-horizon offline trajectories are available?
The paper introduces the Unified Path Planner (UPP), a graph‑search algorithm that balances safety and optimality by adaptively weighting heuristics and using a local inverse‑distance safety field. UPP auto‑tunes its parameters during search, guaranteeing suboptimality bounds while improving obstacle clearance. Evaluation on ten simulated environments shows UPP achieving a 0.94 OptiSafe score—significantly higher than existing methods—while adding only 0.5–1% to path length and maintaining a 100% success rate, with hardware validation on a TurtleBot confirming practical benefits.
The paper introduces SafeHarness, an obstacle‑aware framework that improves the safety of coding agents for robot manipulation. By decomposing tasks into route planning and contact execution, the harness enables the agent to prioritize collision avoidance, achieving 71.9% task success and 87.5% collision avoidance—significantly better than prior methods. The study demonstrates that safety constraints can be effectively integrated into language‑model‑driven robot controllers.
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.
The paper presents a training-free diffusion-based motion planner that replaces learned global trajectory scores with analytical local scores derived from obstacle, smoothness, velocity, and inter-agent feasibility terms. By reconstructing trajectory scores through local interactions between neighboring waypoints and nearby constraints, the method decomposes the denoising process while preserving the optimization structure of classical trajectory methods. Experiments demonstrate that this approach generates smooth, feasible trajectories for large multi-agent tasks in complex environments quickly, outperforming learning-based and optimization baselines without requiring training data.
arXiv:2607. 00065v1 Announce Type: cross Abstract: Any-angle path planning extends traditional graph-based path planning by allowing movement between any pair of vertices, rather than being restricted by predefined edges.
arXiv:2607. 20289v1 Announce Type: cross Abstract: We consider a task planning scenario in which robots sharing a persistent environment are assigned tasks one at a time from a held-out sequence.
arXiv:2606. 19729v2 Announce Type: replace-cross Abstract: Planning under uncertainty is an essential capability for autonomous robots.
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.
Probabilistic Focal Search (PFS) augments traditional Focal Search by probabilistically choosing between the standard heuristic-guided expansion and expanding the minimum‑f node in OPEN. This strategy advances the lower bound, enlarges the FOCAL frontier, and can dramatically reduce node expansions—up to 90% in some benchmarks such as N‑Puzzle and TSP—especially when long f_min plateaus delay useful FOCAL admissions. An anytime variant, APFS, outperforms other tested anytime algorithms on the Generalized Covering TSP, and the same probabilistic scheduler transfers to Dynamic Potential Search as Probabilistic Dynamic Potential Search (PDPS), though its effectiveness varies by domain and bound.