人間のクエリからのブートストラップによる知覚概念の学習
Learning Perceptual Concepts by Bootstrapping from Human Queries
シミュレータの特権情報を使って最小限の人間入力から低次元の幾何学的概念を学び、それを自動ラベラーとして高次元センサデータの大規模データセットを合成することで、人間のラベル負担を抑えつつ実センサ入力で使える知覚概念を学習するフレームワークPCBを提案した。
著者: Andreea Bobu, Chris Paxton, Wei Yang, Balakumar Sundaralingam, Yu-Wei Chao, Maya Cakmak, Dieter Fox
分類: cs.RO, cs.AI, cs.HC, cs.LG
原文アブストラクト
When robots operate in human environments, it's critical that humans can quickly teach them new concepts: object-centric properties of the environment that they care about (e.g. objects near, upright, etc). However, teaching a new perceptual concept from high-dimensional robot sensor data (e.g. point clouds) is demanding, requiring an unrealistic amount of human labels. To address this, we propose a framework called Perceptual Concept Bootstrapping (PCB). First, we leverage the inherently lower-dimensional privileged information, e.g., object poses and bounding boxes, available from a simulator only at training time to rapidly learn a low-dimensional, geometric concept from minimal human input. Second, we treat this low-dimensional concept as an automatic labeler to synthesize a large-scale high-dimensional data set with the simulator. With these two key ideas, PCB alleviates human label burden while still learning perceptual concepts that work with real sensor input where no privileged information is available. We evaluate PCB for learning spatial concepts that describe object state or multi-object relationships, and show it achieves superior performance compared to baseline methods. We also demonstrate the utility of the learned concepts in motion planning tasks on a 7-DoF Franka Panda robot.
