移動を伴う建設タスクのナビゲーションと構築を同時に評価するベンチマーク環境
Simultaneous Navigation and Construction Benchmarking Environments
GPSなしで環境を移動しながら設計通りに構造を変更する移動建設タスクをPOMDPとして定式化し、1/2/3Dグリッドワールドで従来手法と深層強化学習を比較評価したベンチマークを提案する。
著者: Wenyu Han, Chen Feng, Haoran Wu, Alexander Gao, Armand Jordana, Dong Liu, Lerrel Pinto, Ludovic Righetti
分類: cs.RO, cs.AI
原文アブストラクト
We need intelligent robots for mobile construction, the process of navigating in an environment and modifying its structure according to a geometric design. In this task, a major robot vision and learning challenge is how to exactly achieve the design without GPS, due to the difficulty caused by the bi-directional coupling of accurate robot localization and navigation together with strategic environment manipulation. However, many existing robot vision and learning tasks such as visual navigation and robot manipulation address only one of these two coupled aspects. To stimulate the pursuit of a generic and adaptive solution, we reasonably simplify mobile construction as a partially observable Markov decision process (POMDP) in 1/2/3D grid worlds and benchmark the performance of a handcrafted policy with basic localization and planning, and state-of-the-art deep reinforcement learning (RL) methods. Our extensive experiments show that the coupling makes this problem very challenging for those methods, and emphasize the need for novel task-specific solutions.
