次元情報を活用した階層型プログラム合成によるロボット行動選択学習
Robot Action Selection Learning via Layered Dimension Informed Program Synthesis
状態変数の物理次元と演算子の次元制約を利用し、人間が解釈・修正可能なDSLで行動選択ポリシーを直接合成するLDIPSを提案。ロボットサッカーと自動運転で有効性を示し、NN比で学習データを2桁削減、sim2real時の修正も少量で可能とした。
著者: Jarrett Holtz, Arjun Guha, Joydeep Biswas
分類: cs.AI, cs.PL, cs.RO
原文アブストラクト
Action selection policies (ASPs), used to compose low-level robot skills into complex high-level tasks are commonly represented as neural networks (NNs) in the state of the art. Such a paradigm, while very effective, suffers from a few key problems: 1) NNs are opaque to the user and hence not amenable to verification, 2) they require significant amounts of training data, and 3) they are hard to repair when the domain changes. We present two key insights about ASPs for robotics. First, ASPs need to reason about physically meaningful quantities derived from the state of the world, and second, there exists a layered structure for composing these policies. Leveraging these insights, we introduce layered dimension-informed program synthesis (LDIPS) - by reasoning about the physical dimensions of state variables, and dimensional constraints on operators, LDIPS directly synthesizes ASPs in a human-interpretable domain-specific language that is amenable to program repair. We present empirical results to demonstrate that LDIPS 1) can synthesize effective ASPs for robot soccer and autonomous driving domains, 2) requires two orders of magnitude fewer training examples than a comparable NN representation, and 3) can repair the synthesized ASPs with only a small number of corrections when transferring from simulation to real robots.
