用动态梯度引导扩散模型生成无碰撞机械臂轨迹，泛化能力强。
Sum of Costs Diffusion with Dynamic Guidance for Motion Planning
- 用总碰撞代价梯度动态引导扩散过程生成路径
- 在Mπnets数据集上优于所有对比方法，成功率最高
- 适合需要强泛化能力的复杂场景机器人规划
机器人操作中的运动规划问题可通过经典或深度学习方法解决。现有方法在多样化场景中泛化能力不足。本文提出一种具备高泛化能力的方法，利用扩散模型生成无碰撞轨迹，通过总碰撞代价梯度引导去噪过程，并设计动态选择梯度引导起始步数的策略。实验表明，动态引导总碰撞代价可显著提升鲁棒性，克服现有方法的泛化瓶颈。所提模型在Mπnets数据集的多种测试场景中表现最优，优于所有对比方法。
原文摘要 · Abstract (English)
The motion planning problem for robotic manipulation can be addressed through classical or deep learning approaches. Existing methods face significant challenges in generalizing to diverse settings. In this study, we present a method with high generalization capability that generates collision-free trajectories using diffusion models where the denoising process is guided by the gradient of the total collision cost. We are also presenting a dynamic approach for choosing start step of the gradient guidance. Experimental results demonstrate that guiding the diffusion model dynamically with the sum of collision costs offers more robust performance by overcoming the generalization issues faced by competing methods. The proposed model demonstrates its effectiveness by achieving the highest performance on diverse test settings in M$π$nets\ dataset among the compared methods.
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