For planning rearrangements of objects in a clutter, it is required to know the goal configuration of the objects. However, in real life scenarios, this information is not available most of the time. We introduce a novel method that computes a collision-free placement of objects on a cluttered surface, while minimizing the total number and amount of displacements of the existing moveable objects. Our method applies nested local searches that perform multi-objective optimizations guided by heuristics. Experimental evaluations demonstrate high computational efficiency and success rate of our method, as well as good quality of solutions.
@cite_21 aim to find sensible placements for furniture by initially generating a random arrangement, then rearranging it to minimize a cost function that measures the difference between the current arrangement and several positive examples provided by the user. @cite_23 also follow on this idea, and modify the algorithm so it becomes more suitable for robotic applications. Neither study considers heavily cluttered scenes or utilizes high resolution collision checks. @cite_23 , the task is to rearrange objects currently available in the scene to achieve a more tidy arrangement; no new objects are added and there exists no constraints that force a certain set of objects to be on certain surfaces. Furthermore, in these studies, even the initial state is a feasible (collision-free) configuration, and the goal is to improve it in terms of a measure of tidiness.
@cite_21 @cite_23
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We present a system that automatically synthesizes indoor scenes realistically populated by a variety of furniture objects. Given examples of sensibly furnished indoor scenes, our system extracts, in advance, hierarchical and spatial relationships for various furniture objects, encoding them into priors associated with ergonomic factors, such as visibility and accessibility, which are assembled into a cost function whose optimization yields realistic furniture arrangements. To deal with the prohibitively large search space, the cost function is optimized by simulated annealing using a Metropolis-Hastings state search step. We demonstrate that our system can synthesize multiple realistic furniture arrangements and, through a perceptual study, investigate whether there is a significant difference in the perceived functionality of the automatically synthesized results relative to furniture arrangements produced by human designers. We present a method enabling a robot to automatically arrange objects using task and motion planning. Given an input scene consisting of cluttered objects, our method first constructs a target layout of objects as a guidance to the robot for arranging them. For constructing the layout, we use positive examples and pre-extract hierarchical, spatial and pairwise relationships between objects, to understand the user preference on arranging objects. Our method then enables a robot to arrange input objects to reach their target configurations using any task and motion planner. To efficiently arrange the objects, we also propose a priority layer that decides an order of arranging objects to take a small amount of actions. This is achieved by utilizing a dependency graph between objects. We test our method in three different scenes with varying numbers of objects, and apply our method to two well-known task and motion planners with the virtual PR2 robot. We demonstrate that we can use the robot to automatically arrange objects, and show that our priority layer reduces the total running time up to 2.15 times in those tested planners.