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.

本文介绍了将深度生成建模与任务规划相结合的方法，以解决在未知环境中的自主机器人操作规划中运用传统TAMP方法中存在的问题。通过使用扩散模型学习约束条件和采样器，并将其与TAMP求解器组合使用以满足计划中的约束条件。同时，为了在这些约束条件满足不确定物体状态的情况下进行预测，在学习的低维潜在空间中定义这些采样器。实验结果中介绍了使用传统TAMP，生成式学习和潜在嵌入组合的长程约束条件推理方法在一个物体操作环境中的有效性。

DiMSam：部分可观察性下任务与动作规划的扩散模型采样器