Leveraging Experience in Lazy Search

  title={Leveraging Experience in Lazy Search},
  author={M. Bhardwaj and S. Choudhury and B. Boots and S. Srinivasa},
  • M. Bhardwaj, S. Choudhury, +1 author S. Srinivasa
  • Published 2019
  • Engineering, Computer Science
  • ArXiv
  • Lazy graph search algorithms are efficient at solving motion planning problems where edge evaluation is the computational bottleneck. These algorithms work by lazily computing the shortest potentially feasible path, evaluating edges along that path, and repeating until a feasible path is found. The order in which edges are selected is critical to minimizing the total number of edge evaluations: a good edge selector chooses edges that are not only likely to be invalid, but also eliminates future… CONTINUE READING
    Differentiable Gaussian Process Motion Planning
    • 1
    • PDF


    Publications referenced by this paper.
    Learning Heuristic Search via Imitation
    • 27
    • PDF
    A Unifying Formalism for Shortest Path Problems with Expensive Edge Evaluations via Lazy Best-First Search over Paths with Edge Selectors
    • 34
    • Highly Influential
    • PDF
    Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs
    • 11
    • PDF
    Lazy Receding Horizon A* for Efficient Path Planning in Graphs with Expensive-to-Evaluate Edges
    • 14
    • PDF
    Heuristic Search on Graphs with Existence Priors for Expensive-to-Evaluate Edges
    • 10
    • PDF
    Data-driven planning via imitation learning
    • 22
    • PDF
    Bayesian Active Edge Evaluation on Expensive Graphs
    • 6
    • PDF
    Path planning using lazy PRM
    • 716
    • PDF