13 research outputs found
The Provable Virtue of Laziness in Motion Planning
The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms
that only evaluate edges along shortest paths between the source and target.
These algorithms were designed to minimize the number of edge evaluations in
settings where edge evaluation dominates the running time of the algorithm; but
how close to optimal are LazySP algorithms in terms of this objective? Our main
result is an analytical upper bound, in a probabilistic model, on the number of
edge evaluations required by LazySP algorithms; a matching lower bound shows
that these algorithms are asymptotically optimal in the worst case
Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles
Lazy search algorithms can efficiently solve problems where edge evaluation
is the bottleneck in computation, as is the case for robotic motion planning.
The optimal algorithm in this class, LazySP, lazily restricts edge evaluation
to only the shortest path. Doing so comes at the expense of search effort,
i.e., LazySP must recompute the search tree every time an edge is found to be
invalid. This becomes prohibitively expensive when dealing with large graphs or
highly cluttered environments. Our key insight is the need to balance both edge
evaluation and search effort to minimize the total planning time. Our
contribution is two-fold. First, we propose a framework, Generalized Lazy
Search (GLS), that seamlessly toggles between search and evaluation to prevent
wasted efforts. We show that for a choice of toggle, GLS is provably more
efficient than LazySP. Second, we leverage prior experience of edge
probabilities to derive GLS policies that minimize expected planning time. We
show that GLS equipped with such priors significantly outperforms competitive
baselines for many simulated environments in R2, SE(2) and 7-DoF manipulation.Comment: Submitted to International Conference on Automated Planning and
Scheduling (ICAPS) 201
Policy-Guided Lazy Search with Feedback for Task and Motion Planning
PDDLStream solvers have recently emerged as viable solutions for Task and
Motion Planning (TAMP) problems, extending PDDL to problems with continuous
action spaces. Prior work has shown how PDDLStream problems can be reduced to a
sequence of PDDL planning problems, which can then be solved using
off-the-shelf planners. However, this approach can suffer from long runtimes.
In this paper we propose LAZY, a solver for PDDLStream problems that maintains
a single integrated search over action skeletons, which gets progressively more
geometrically informed as samples of possible motions are lazily drawn during
motion planning. We explore how learned models of goal-directed policies and
current motion sampling data can be incorporated in LAZY to adaptively guide
the task planner. We show that this leads to significant speed-ups in the
search for a feasible solution evaluated over unseen test environments of
varying numbers of objects, goals, and initial conditions. We evaluate our TAMP
approach by comparing to existing solvers for PDDLStream problems on a range of
simulated 7DoF rearrangement/manipulation problems
Toward Asymptotically-Optimal Inspection Planning via Efficient Near-Optimal Graph Search
Inspection planning, the task of planning motions that allow a robot to
inspect a set of points of interest, has applications in domains such as
industrial, field, and medical robotics. Inspection planning can be
computationally challenging, as the search space over motion plans that inspect
the points of interest grows exponentially with the number of inspected points.
We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS),
that computes inspection plans whose length and set of inspected points
asymptotically converge to those of an optimal inspection plan. IRIS
incrementally densifies a motion planning roadmap using sampling-based
algorithms, and performs efficient near-optimal graph search over the resulting
roadmap as it is generated. We demonstrate IRIS's efficacy on a simulated
planar 5DOF manipulator inspection task and on a medical endoscopic inspection
task for a continuum parallel surgical robot in anatomy segmented from patient
CT data. We show that IRIS computes higher-quality inspection paths orders of
magnitudes faster than a prior state-of-the-art method.Comment: RSS 201