用流模型提升轨迹拼接能力，让机器人在新场景下也能避障规划。
Improving Trajectory Stitching with Flow Models
- 引入流模型改进轨迹拼接架构与训练数据选择
- 在仿真和真实机械臂上实现更大障碍物避让，性能显著优于基线
- 适合需要灵活轨迹生成的机器人任务，如复杂环境规划
生成模型在轨迹规划中展现出巨大潜力，因其能建模复杂分布并支持引导推理。以往方法在机器人操作中表现良好，但在训练集中不存在完整轨迹时表现不佳。我们发现这是由于无法进行轨迹拼接所致，进而提出解决该问题的架构与数据集设计，并改进训练与推理流程以增强稳定性与能力。通过在仿真及真实Franka Panda机械臂上生成分布外边界条件下的规划路径并实现障碍物避让，实验表明本方法显著优于基线，可避开四倍大的障碍物。
原文摘要 · Abstract (English)
Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to remedy this. On top of this, we propose a novel addition to the training and inference procedures to both stabilize and enhance these capabilities. We demonstrate the efficacy of our approach by generating plans with out of distribution boundary conditions and performing obstacle avoidance on the Franka Panda in simulation and on real hardware. In both of these tasks our method performs significantly better than the baselines and is able to avoid obstacles up to four times as large.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。