GNNと時間エンコーディングを用いた動的環境における学習ベース動作計画
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
動的障害物の軌跡を考慮する必要がある動作計画問題に対し、GNNと時間エンコーディング、模倣学習を組み合わせて計画を高速化する手法を提案した。衝突チェックを大幅に削減し、成功率も維持できることを示した。
著者: Ruipeng Zhang, Chenning Yu, Jingkai Chen, Chuchu Fan, Sicun Gao
分類: cs.RO, cs.AI
原文アブストラクト
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of planning in dynamic environments, such as multi-arm assembly tasks and human-robot interaction, motion planners need to consider the trajectories of the dynamic obstacles and reason about temporal-spatial interactions in very large state spaces. We propose a GNN-based approach that uses temporal encoding and imitation learning with data aggregation for learning both the embeddings and the edge prioritization policies. Experiments show that the proposed methods can significantly accelerate online planning over state-of-the-art complete dynamic planning algorithms. The learned models can often reduce costly collision checking operations by more than 1000x, and thus accelerating planning by up to 95%, while achieving high success rates on hard instances as well.
