グラフベースの散らかったシーン生成と深層強化学習による対話的探索
Graph-based Cluttered Scene Generation and Interactive Exploration using Deep Reinforcement Learning
シーングラマとGNNで多様な散らかったシーンを生成し、深層強化学習で隠れた物体を効率的に探索するロボット方策を学習、実機UR10でも検証した論文。
著者: K. Niranjan Kumar, Irfan Essa, Sehoon Ha
分類: cs.RO
原文アブストラクト
We introduce a novel method to teach a robotic agent to interactively explore cluttered yet structured scenes, such as kitchen pantries and grocery shelves, by leveraging the physical plausibility of the scene. We propose a novel learning framework to train an effective scene exploration policy to discover hidden objects with minimal interactions. First, we define a novel scene grammar to represent structured clutter. Then we train a Graph Neural Network (GNN) based Scene Generation agent using deep reinforcement learning (deep RL), to manipulate this Scene Grammar to create a diverse set of stable scenes, each containing multiple hidden objects. Given such cluttered scenes, we then train a Scene Exploration agent, using deep RL, to uncover hidden objects by interactively rearranging the scene. We show that our learned agents hide and discover significantly more objects than the baselines. We present quantitative results that prove the generalization capabilities of our agents. We also demonstrate sim-to-real transfer by successfully deploying the learned policy on a real UR10 robot to explore real-world cluttered scenes. The supplemental video can be found at https://www.youtube.com/watch?v=T2Jo7wwaXss.
