自动生成可执行的多步骤机器人操作任务
PRAG: Procedural Action Generator
- 根据用户输入的原子动作与空间关系，生成符合逻辑和物理规则的任务序列
- 通过符号与物理双重验证，确保生成任务在真实机器人环境中可解，支持15步以内任务生成
- 适合强化学习训练，提供密集奖励与任务相似性度量，可直接对接现有框架
我们提出一种新方法，用于在机器人领域中程序化构建多步骤、高接触性的操作任务。该生成器接受用户定义的原子动作、物体及空间谓词作为输入，输出指定长度且可解的任务，适配选定的机器人环境。生成过程通过符号验证与物理验证双重约束，排除所有不可解组合：符号验证检查序列的逻辑一致性与物体重合关系合理性；物理验证则评估任务在目标机器人环境中的可执行性。仅通过双验证的任务被保留。生成结果可直接接入任意现有训练框架，或存储为包含详细信息的结构化任务数据集，支持强化学习训练中密集奖励函数、子目标状态对齐与任务语义相似性度量。我们在最多15步的动作序列上测试，成功生成数百万个唯一可解任务。
原文摘要 · Abstract (English)
We present a novel approach for the procedural construction of multi-step contact-rich manipulation tasks in robotics. Our generator takes as input user-defined sets of atomic actions, objects, and spatial predicates and outputs solvable tasks of a given length for the selected robotic environment. The generator produces solvable tasks by constraining all possible (nonsolvable) combinations by symbolic and physical validation. The symbolic validation checks each generated sequence for logical and operational consistency, and also the suitability of object-predicate relations. Physical validation checks whether tasks can be solved in the selected robotic environment. Only the tasks that passed both validators are retained. The output from the generator can be directly interfaced with any existing framework for training robotic manipulation tasks, or it can be stored as a dataset of curated robotic tasks with detailed information about each task. This is beneficial for RL training as there are dense reward functions and initial and goal states paired with each subgoal. It allows the user to measure the semantic similarity of all generated tasks. We tested our generator on sequences of up to 15 actions resulting in millions of unique solvable multi-step tasks.
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