Long-HOT: 長期的な物体輸送のためのモジュール型階層的アプローチ
Long-HOT: A Modular Hierarchical Approach for Long-Horizon Object Transport
長期的な探索とナビゲーションを必要とする物体輸送タスクを提案し、トポロジカルグラフと階層的方策を用いたモジュール型フレームワークで効率的に解決する。
著者: Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker
分類: cs.CV, cs.RO
原文アブストラクト
We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning where the agent is required to efficiently find and pick up target objects to be carried and dropped at a goal location, with load constraints and optional access to a container if it finds one. Further, we propose a modular hierarchical transport policy (HTP) that builds a topological graph of the scene to perform exploration with the help of weighted frontiers. Our hierarchical approach uses a combination of motion planning algorithms to reach point goals within explored locations and object navigation policies for moving towards semantic targets at unknown locations. Experiments on both our proposed Habitat transport task and on MultiOn benchmarks show that our method significantly outperforms baselines and prior works. Further, we validate the effectiveness of our modular approach for long-horizon transport by demonstrating meaningful generalization to much harder transport scenes with training only on simpler versions of the task.
