论文标题
使用GNN和时间编码在动态环境中基于学习的运动计划
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
论文作者
论文摘要
基于学习的方法已显示出有希望的加速运动计划的表现，但主要是在静态环境的环境中。对于动态环境中计划的更具挑战性的问题，例如多臂组装任务和人类机器人的互动，运动计划者需要考虑动态障碍的轨迹和有关时间空间互动的轨迹。我们提出了一种基于GNN的方法，该方法使用时间编码和模仿学习与数据聚合一起学习嵌入和边缘优先级策略。实验表明，所提出的方法可以大大加速在线计划，以实现最新的完整动态计划算法。博学的模型通常可以将昂贵的碰撞检查操作减少超过1000倍，从而使计划加速多达95％，同时在硬实例上也达到了高成功率。
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.
