用扩散模型生成机器人运动路径，速度比传统方法快10倍，成功率超90%。
Keypoint-based Diffusion for Robotic Motion Planning on the NICOL Robot
- 基于关键点的扩散模型替代传统数值规划，提升推理效率。
- 无需点云输入仍达90%无碰撞成功率，推理时间降为原方法的十分之一。
- 适用于对实时性要求高的机器人路径规划场景。
我们提出一种新型基于扩散模型的机器人运动规划方法。传统数值规划方法虽通用但计算耗时长。本工作利用深度学习从规划器生成的数据集上训练，显著缩短运行时间。初始模型使用点云嵌入作为输入预测关键点关节序列，但消融实验发现点云条件化效果不佳，经识别并修正数据集偏差后性能提升。最终模型即使不依赖点云编码，运行时间仍比数值方法快一个数量级，测试集上达到最高90%的无碰撞成功率达。
原文摘要 · Abstract (English)
We propose a novel diffusion-based action model for robotic motion planning. Commonly, established numerical planning approaches are used to solve general motion planning problems, but have significant runtime requirements. By leveraging the power of deep learning, we are able to achieve good results in a much smaller runtime by learning from a dataset generated by these planners. While our initial model uses point cloud embeddings in the input to predict keypoint-based joint sequences in its output, we observed in our ablation study that it remained challenging to condition the network on the point cloud embeddings. We identified some biases in our dataset and refined it, which improved the model's performance. Our model, even without the use of the point cloud encodings, outperforms numerical models by an order of magnitude regarding the runtime, while reaching a success rate of up to 90% of collision free solutions on the test set.
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