Classical methods in robot motion planning, such as sampling-based and optimization-based methods, often struggle with scalability towards higher-dimensional state spaces and complex environments. Diffusion models, known for their capability to learn complex, high-dimensional and multi-modal data distributions, provide a promising alternative when applied to motion planning problems and have already shown interesting results. However, most of the current approaches train their model for a single environment, limiting their generalization to environments not seen during training. The techniques that do train a model for multiple environments rely on a specific camera to provide the model with the necessary environmental information and therefore always require that sensor. To effectively adapt to diverse scenarios without the need for retraining, this research proposes Context-Aware Motion Planning Diffusion (CAMPD). CAMPD leverages a classifier-free denoising probabilistic diffusion model, conditioned on sensor-agnostic contextual information. An attention mechanism, integrated in the well-known U-Net architecture, conditions the model on an arbitrary number of contextual parameters. CAMPD is evaluated on a 7-DoF robot manipulator and benchmarked against state-of-the-art approaches on real-world tasks, showing its ability to generalize to unseen environments and generate high-quality, multi-modal trajectories, at a fraction of the time required by existing methods.

翻译：经典机器人运动规划方法，如基于采样和基于优化的方法，在处理高维状态空间和复杂环境时往往面临可扩展性不足的问题。扩散模型以其能够学习复杂高维多模态数据分布的特性，为运动规划问题提供了有前景的替代方案，并已展现出令人瞩目的成果。然而，当前多数方法仅针对单一环境训练模型，限制了其在未见过环境中的泛化能力。那些在多种环境中训练模型的技术需依赖特定相机提供环境信息，因此始终需要该传感器。为有效适应不同场景且无需重新训练，本研究提出上下文感知运动规划扩散模型（CAMPD）。CAMPD采用无分类器去噪概率扩散模型，以传感器无关的上下文信息为条件。通过集成在著名U-Net架构中的注意力机制，该模型能够基于任意数量的上下文参数进行条件化。CAMPD在7自由度机器人操作臂上进行了评估，并在实际任务中与最先进方法进行了基准对比，结果显示其能够在极短时间内（仅为现有方法所需时间的零头）泛化至未见环境并生成高质量多模态轨迹。