Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of latent planning systems. We rely on our Latent Space Roadmap (LSR) framework, which builds a graph in a learned structured latent space to perform planning. Given multiple LSR framework instances, that differ either on their latent spaces or on the parameters for constructing the graph, we use the action information as well as the embedded nodes of the produced plans to define similarity measures. These are then utilized to select the most promising plans. We validate the performance of our Ensemble LSR (ENS-LSR) on simulated box stacking and grape harvesting tasks as well as on a real-world robotic T-shirt folding experiment.

翻译：学习到的潜在空间中的规划有助于降低原始观测的维度。本文提出利用集成范式增强潜在规划系统的鲁棒性。我们基于潜在空间路线图（LSR）框架，该框架在学到的结构化潜在空间中构建图以执行规划。针对多个在潜在空间或图构建参数上存在差异的LSR框架实例，我们利用动作信息以及生成计划的嵌入节点定义相似性度量，进而选择最优计划。我们在模拟的箱子堆叠和葡萄采摘任务以及真实世界的机器人T恤折叠实验中验证了集成LSR（ENS-LSR）的性能。