Task and Motion Planning (TAMP) approaches are effective at planning long-horizon autonomous robot manipulation. However, because they require a planning model, it can be difficult to apply them to domains where the environment and its dynamics are not fully known. We propose to overcome these limitations by leveraging deep generative modeling, specifically diffusion models, to learn constraints and samplers that capture these difficult-to-engineer aspects of the planning model. These learned samplers are composed and combined within a TAMP solver in order to find action parameter values jointly that satisfy the constraints along a plan. To tractably make predictions for unseen objects in the environment, we define these samplers on low-dimensional learned latent embeddings of changing object state. We evaluate our approach in an articulated object manipulation domain and show how the combination of classical TAMP, generative learning, and latent embeddings enables long-horizon constraint-based reasoning.

翻译：任务与运动规划（TAMP）方法在规划长时域自主机器人操作方面效果显著。然而，由于这类方法需要规划模型，其难以应用于环境及其动力学特性不完全已知的领域。我们提出通过利用深度生成模型（特别是扩散模型）来学习规划模型中难以工程化的约束与采样器，从而克服这些限制。这些学习到的采样器在TAMP求解器中被组合与集成，以联合找到满足规划路径约束的动作参数值。为对环境中的未知物体进行可推断预测，我们将这些采样器定义在低维度的学习潜嵌入上，该嵌入表征变化中的物体状态。我们在铰接式物体操作领域评估了该方法，并展示了经典TAMP、生成式学习与潜嵌入相结合如何实现长时域基于约束的推理。