Dijkstra as an Oracle for Online Stochastic Shortest Path Navigation with Provable Guarantees

• URL: http://arxiv.org/abs/2608.17703v1
• Date: Tue, 18 Aug 2026 12:26:17 GMT
• Title: Dijkstra as an Oracle for Online Stochastic Shortest Path Navigation with Provable Guarantees
• Abstract summary: This study shows that Dijkstra's algorithm can remain an exact planning engine under a condition that is much weaker than the causality condition often invoked in the literature.<n>Building on this characterization, an online learner DORA is proposed for robot navigation that calls a shortest path oracle a fixed number of times per episode.
• Score: 0.5735035463793009
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. Planners that solve the underlying stochastic shortest path problem exactly, such as value iteration, require computation that grows with the diameter of the map, whereas Dijkstra's algorithm is fast but is usually considered inexact once transitions are stochastic. This study shows that Dijkstra's algorithm can remain an exact planning engine under a condition that is much weaker than the causality condition often invoked in the literature, namely nonnegativity of a reduced cost defined on the determinized map. Building on this characterization, an online learner DORA (Dijkstra Oracle Reduced-cost Algorithm) is proposed for robot navigation that calls a shortest path oracle a fixed number of times per episode, never estimates a transition kernel, and adds a logarithmic survival weight when the probability of contact with a dynamic obstacle must stay within a budget. In the numerical experiments involving three other benchmarks that cover grid world navigation, directional drilling, and drone surveillance, the learner matches optimistic value iteration that is given the true transition kernel while performing 4.5 to 19.3 times less planner work, reduces contacts during learning by a factor of seventeen relative to determinize and replan, and keeps the contact rate within budgets that span two orders of magnitude. These results indicate that shortest path search supports safe and efficient online navigation and path planning tasks.

Related papers

• Traffic-Aware Navigation in Road Networks [0.0]
  This project compares three graph search approaches for the task of traffic-aware navigation in Kingston's road network.<n>Dijkstra's and A* resulted in the most traffic-aware optimal solutions with minimal preprocessing required.<n>Floyd-Warshall-Ingerman was the fastest in real time but provided distance based paths with no traffic awareness.
  arXiv  Detail & Related papers  (2026-02-02T14:35:49Z)
• Local Path Optimization in The Latent Space Using Learned Distance Gradient [14.220732305518583]
  Constrained motion planning is a common but challenging problem in robotic manipulation.<n>In this paper, we propose a method that trains a neural network to predict the minimum distance between the robot and obstacles.<n>The learned distance gradient is then used to calculate the direction of movement in the latent space to move the robot away from obstacles.
  arXiv  Detail & Related papers  (2025-12-30T14:56:09Z)
• TransPath: Learning Heuristics For Grid-Based Pathfinding via Transformers [64.88759709443819]
  We suggest learning the instance-dependent proxies that are supposed to notably increase the efficiency of the search. The first proxy we suggest to learn is the correction factor, i.e. the ratio between the instance independent cost-to-go estimate and the perfect one. The second proxy is the path probability, which indicates how likely the grid cell is lying on the shortest path.
  arXiv  Detail & Related papers  (2022-12-22T14:26:11Z)
• Learning Graph Search Heuristics [48.83557172525969]
  We present PHIL (Path Heuristic with Imitation Learning), a novel neural architecture and a training algorithm for discovering graph search and navigations from data. Our function learns graph embeddings useful for inferring node distances, runs in constant time independent of graph sizes, and can be easily incorporated in an algorithm such as A* at test time. Experiments show that PHIL reduces the number of explored nodes compared to state-of-the-art methods on benchmark datasets by 58.5% on average.
  arXiv  Detail & Related papers  (2022-12-07T22:28:00Z)
• Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map [39.54575497596679]
  This work proposes a new representation of traversability based exclusively on robot speed that can be learned from data. The proposed algorithm learns to predict a distribution of speeds the robot could achieve, conditioned on the environment semantics and commanded speed. Numerical simulations demonstrate that the proposed risk-aware planning algorithm leads to faster average time-to-goals.
  arXiv  Detail & Related papers  (2022-03-25T03:08:02Z)
• Systematic Comparison of Path Planning Algorithms using PathBench [55.335463666037086]
  Path planning is an essential component of mobile robotics. Development of learning-based path planning algorithms has been experiencing rapid growth. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2022-03-07T01:52:57Z)
• Online estimation and control with optimal pathlength regret [52.28457815067461]
  A natural goal when designing online learning algorithms is to bound the regret of the algorithm in terms of the temporal variation of the input sequence. Data-dependent "pathlength" regret bounds have recently been obtained for a wide variety of online learning problems, including OCO and bandits.
  arXiv  Detail & Related papers  (2021-10-24T22:43:15Z)
• PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms [59.3879573040863]
  Path planning is a key component in mobile robotics. Few attempts have been made to benchmark the algorithms holistically or unify their interface. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2021-05-04T21:48:18Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Performance Improvement of Path Planning algorithms with Deep Learning Encoder Model [1.1995939891389429]
  Convolutional Neural Network (CNN) was used to overcome this situation. This paper analyzes in-depth the performance to eliminate the useless paths using this CNN model.
  arXiv  Detail & Related papers  (2020-08-05T17:34:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.