15 research outputs found
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A*
Effectively performing object rearrangement is an essential skill for mobile
manipulators, e.g., setting up a dinner table or organizing a desk. A key
challenge in such problems is deciding an appropriate manipulation order for
objects to effectively untangle dependencies between objects while considering
the necessary motions for realizing the manipulations (e.g., pick and place).
To our knowledge, computing time-optimal multi-object rearrangement solutions
for mobile manipulators remains a largely untapped research direction. In this
research, we propose ORLA*, which leverages delayed (lazy) evaluation in
searching for a high-quality object pick and place sequence that considers both
end-effector and mobile robot base travel. ORLA* also supports multi-layered
rearrangement tasks considering pile stability using machine learning.
Employing an optimal solver for finding temporary locations for displacing
objects, ORLA* can achieve global optimality. Through extensive simulation and
ablation study, we confirm the effectiveness of ORLA* delivering quality
solutions for challenging rearrangement instances. Supplementary materials are
available at: https://gaokai15.github.io/ORLA-Star/Comment: Submitted to ICRA 202
Rearrangement on Lattices with Pick-n-Swaps: Optimality Structures and Efficient Algorithms
We propose and study a class of rearrangement problems under a novel
pick-n-swap prehensile manipulation model, in which a robotic manipulator,
capable of carrying an item and making item swaps, is tasked to sort items
stored in lattices of variable dimensions in a time-optimal manner. We
systematically analyze the intrinsic optimality structure, which is fairly rich
and intriguing, under different levels of item distinguishability (fully
labeled, where each item has a unique label, or partially labeled, where
multiple items may be of the same type) and different lattice dimensions.
Focusing on the most practical setting of one and two dimensions, we develop
low polynomial time cycle-following based algorithms that optimally perform
rearrangements on 1D lattices under both fully- and partially-labeled settings.
On the other hand, we show that rearrangement on 2D and higher dimensional
lattices becomes computationally intractable to optimally solve. Despite their
NP-hardness, we prove that efficient cycle-following based algorithms remain
asymptotically optimal for 2D fully- and partially-labeled settings, in
expectation, using the interesting fact that random permutations induce only a
small number of cycles. We further improve these algorithms to provide
1.x-optimality when the number of items is small. Simulation studies
corroborate the effectiveness of our algorithms.Comment: To appear in R:SS 202
On Rearrangement of Items Stored in Stacks
There are stacks, each filled with items, and one empty stack.
Every stack has capacity . A robot arm, in one stack operation (step),
may pop one item from the top of a non-empty stack and subsequently push it
onto a stack not at capacity. In a {\em labeled} problem, all items are
distinguishable and are initially randomly scattered in the stacks. The
items must be rearranged using pop-and-pushs so that in the end, the stack holds items , in that order, from the top to
the bottom for all . In an {\em unlabeled} problem, the
items are of types of each. The goal is to rearrange items so that
items of type are located in the stack for all . In carrying out the rearrangement, a natural question is to find the least
number of required pop-and-pushes.
Our main contributions are: (1) an algorithm for restoring the order of
items stored in an table using only column and row
permutations, and its generalization, and (2) an algorithm with a guaranteed
upper bound of steps for solving both versions of the stack
rearrangement problem when for arbitrary fixed
positive number . In terms of the required number of steps, the labeled and
unlabeled version have lower bounds
and , respectively