In this paper, we propose using deep neural architectures (i.e., vision transformers and ResNet) as heuristics for sequential decision-making in robotic manipulation problems. This formulation enables predicting the subset of objects that are relevant for completing a task. Such problems are often addressed by task and motion planning (TAMP) formulations combining symbolic reasoning and continuous motion planning. In essence, the action-object relationships are resolved for discrete, symbolic decisions that are used to solve manipulation motions (e.g., via nonlinear trajectory optimization). However, solving long-horizon tasks requires consideration of all possible action-object combinations which limits the scalability of TAMP approaches. To overcome this combinatorial complexity, we introduce a visual perception module integrated with a TAMP-solver. Given a task and an initial image of the scene, the learned model outputs the relevancy of objects to accomplish the task. By incorporating the predictions of the model into a TAMP formulation as a heuristic, the size of the search space is significantly reduced. Results show that our framework finds feasible solutions more efficiently when compared to a state-of-the-art TAMP solver.

翻译：本文提出使用深度神经网络架构（即视觉Transformer和ResNet）作为机器人操作问题中顺序决策的启发式方法。该公式能够预测与完成任务相关的对象子集。此类问题通常通过结合符号推理与连续运动规划的任务与运动规划（TAMP）框架来解决。本质上，动作-对象关系通过离散的符号决策解析，进而用于求解操作运动（例如通过非线性轨迹优化）。然而，解决长时域任务需要考虑所有可能的动作-对象组合，这限制了TAMP方法的可扩展性。为克服这种组合爆炸问题，我们引入一个与TAMP求解器集成的视觉感知模块。给定任务和场景初始图像，学习模型输出对象对于完成任务的相关性。通过将模型预测作为启发式信息融入TAMP框架，搜索空间规模显著降低。结果表明，与最先进的TAMP求解器相比，本框架能更高效地求得可行解。