用强化学习动态修剪机器人探索中的冗余图结构。
Learning-Guided Sparsification of Dynamic Graphs in Robotic Exploration
- 用基于Transformer的强化学习策略实时剪枝动态图
- 图大小最多减少96%，提升探索与计算效率
- 适合复杂长时程任务的机器人探索系统
许多机器人探索算法依赖图结构进行前缘探索和动态路径规划，但这些图会迅速膨胀，积累冗余信息并影响性能。本文提出一种基于Transformer的混合框架，通过近端策略优化（Proximal Policy Optimization）训练，可在探索过程中剪枝图结构，限制其增长并减少信息过载。在使用快速扩展随机树（Rapidly-Exploring Random Trees）进行前缘探索的仿真中，所学策略使图规模最多减少96%。初步证据表明，该框架能在低信息密度下实现有效且可泛化的探索，持续优于随机剪枝基线，并在复杂长时程任务中同时提升探索效率与计算效率。据我们所知，这是首个表明强化学习可用于剪枝机器人探索中动态图的成果。
原文摘要 · Abstract (English)
Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning. However, these graphs grow rapidly, accumulating redundant information and impacting performance. We present a hybrid transformer-based framework trained with Proximal Policy Optimization which complements exploration algorithms by pruning these graphs during exploration, limiting their growth and reducing the accumulation of excess information. The framework was evaluated on simulations of a robotic agent using Rapidly-Exploring Random Trees to carry out frontier-based exploration, where the learned policy reduces graph size by up to 96%. We find preliminary evidence that our framework enables effective, generalizable exploration under reduced information density, consistently outperforming the randomly pruned baseline and improving both exploration efficiency and computational efficiency beyond the unpruned baseline for complex long-horizon exploration tasks. To the best of our knowledge, these results are the first suggesting the viability of RL to prune dynamic graphs used in robotic exploration algorithms.
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