DeepAI
Log In Sign Up

Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting

12/15/2019
by   Haoran Song, et al.
0

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated sorting tasks and observe its effectiveness in reliably sorting up to 40 convex objects. In addition, we observe that the algorithm is capable to also sort non-convex objects, as well as convex objects in the presence of immovable obstacles.

READ FULL TEXT

page 1

page 5

03/09/2020

Convex Hull Monte-Carlo Tree Search

This work investigates Monte-Carlo planning for agents in stochastic env...
11/23/2022

Ordered sorting of cluttered objects using multiple mobile manipulators

We present a search-based planning algorithm to sort objects in clutter ...
12/29/2020

Object sorting using faster R-CNN

In a factory production line, different industry parts need to be quickl...
02/03/2022

Self-Supervised Monte Carlo Tree Search Learning for Object Retrieval in Clutter

In this study, working with the task of object retrieval in clutter, we ...
12/22/2022

Scaffolding Generation using a 3D Physarum Polycephalum Simulation

In this demo, we present a novel technique for approximating topological...
10/04/2022

Persistent Homology Guided Monte-Carlo Tree Search for Effective Non-Prehensile Manipulation

Performing object retrieval tasks in messy real-world workspaces involve...