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:在学习的潜在空间中进行规划有助于降低原始观测数据的维度。在本研究中,我们提出利用集成范例来增强潜在规划系统的鲁棒性。我们依赖于我们的潜空间路线图(LSR)框架,在学习的结构化潜在空间中构建图形来执行规划。给定多个 LSR 框架实例,它们在潜在空间或构建图形的参数上不同,我们利用行动信息和产生的计划的嵌入节点来定义相似性度量。然后利用这些度量来选择最有前途的计划。我们对模拟的箱子叠放和采摘葡萄任务以及实际的机器人折叠 T 恤实验的表现进行了验证。