用大模型生成海量机器人运动数据，训练出高效精准的神经规划器。
PerFACT: Motion Policy with LLM-Powered Dataset Synthesis and Fusion Action-Chunking Transformers
- 用大模型自动设计工作空间并生成350万条轨迹数据
- 融合多模态信号的分块变换器实现亚秒级推理
- 适合需要快速部署的复杂场景机器人应用
深度学习方法通过利用规划数据集中的先验经验显著提升了机械臂运动规划性能。然而，当前最先进的神经运动规划器主要在人工生成的工作空间中收集的小规模数据集上训练，限制了其在日常场景中的部署。此外，这些规划器常依赖于难以编码关键规划信息的单体网络结构。为此，我们提出基于大语言模型（LLM）驱动的数据集合成与融合动作分块变换器的运动策略（PerFACT），包含两个核心组件：首先，一种新颖的工作空间生成方法，通过程序化基础元素生成及大模型辅助建议与布局，实现大规模规划数据采集；其次，引入端到端、开环的神经运动规划器——融合动作分块变换器（MπNetsFusion），能更好编码规划信号并关注多种特征模态。借助PerFACT，我们构建了一个包含350万条轨迹的数据集，用于训练和评估MπNetsFusion，结果表明其在保持与采样类及端到端神经基准规划器相当性能的同时，实现了稳定的低规划时间，推理时间低于1秒。
原文摘要 · Abstract (English)
Deep learning methods have significantly enhanced motion planning for robotic manipulators by leveraging prior experiences within planning datasets. However, state-of-the-art neural motion planners are primarily trained on small datasets collected in manually generated workspaces, limiting their deployment in various everyday scenarios. Additionally, these planners often rely on monolithic network architectures that struggle to encode critical planning information. To address these challenges, we introduce Motion Policy with Dataset Synthesis powered by large language models (LLMs) and Fusion Action-Chunking Transformers (PerFACT), which incorporates two key components. Firstly, a novel workspace generation method, PerFACT, enables large-scale planning data collection by leveraging procedural primitive generation, and LLM-powered primitive suggestion and placement. Secondly, we introduce Fusion Motion Policy Networks (M$π$NetsFusion), an end-to-end, open-loop neural motion planner that uses a fusion action-chunking transformer to better encode planning signals and attend to multiple feature modalities. Leveraging PerFACT, we collect a dataset of 3.5M trajectories to train and evaluate M$π$NetsFusion against state-of-the-art planners. Results show that M$π$NetsFusion achieves consistently low planning time with sub-second inference, while maintaining competitive performance compared to both sampling-based and end-to-end neural benchmark planners. Project website: \href{https://davoodsz.github.io/perfact.github.io/}{https://davoodsz.github.io/perfact.github.io/}
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