動作計画フィードバックから汎用的な再配置ヒューリスティックを学習するREAP
REAP: Learning General Rearrangement Heuristics from Motion-Planner Feedback
シンボリック抽象化上の関係グラフニューラルネットワークヒューリスティックを、動作計画器を教師として学習させ、幾何的制約を考慮した再配置計画を効率的に解くフレームワーク。
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
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著者: Daniel Swoboda, Hector Geffner
分類: cs.RO, cs.AI
原文アブストラクト
Robotic rearrangement tasks require planning sequences of actions that fulfill high-level specifications while also being geometrically feasible. Symbolic abstractions make the combinatorial structure of rearrangement tasks tractable, but they usually do not fully capture the geometric constraints that determine which actions a robot can actually realize. These constraints are hard to express in the symbolic model. We propose REAP (Rearrangement with Elicited Action Preconditions), a framework in which a relational graph neural network heuristic is learned over a simple symbolic abstraction. A motion planner serves as a supervisor that elicits the hidden geometric preconditions of the problem during training. Deployed with greedy best-first search, the learned heuristic generalizes out-of-distribution along object count, plan length, and object arrangement, and can be trained jointly on several domains. Compared to classical heuristic search and learning-based baselines, REAP solves more problems, requires roughly an order of magnitude fewer motion-planner queries than comparable methods, and the action it ranks first is geometrically feasible in 94% of sampled states, against 22% for a random choice.
