TaPeR：从少量演示中概率恢复稀疏任务优先关系图
TaPeR: Probabilistic Recovery of Sparse Task Precedence Graphs from a Handful of Demonstrations
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中文总结 AI 辅助
TaPeR方法仅用运动学图和物体位姿分布，从少量演示中恢复更准确的稀疏任务优先关系图，可生成机器人的多种有效执行顺序。
中文摘要 AI 辅助
长程操纵任务通常仅部分有序，例如组装电子设备时，电池和电路板可按任意顺序安装，但二者都必须到位后才能合上外壳。恢复此类依赖关系能让机器人灵活调整子任务顺序，同时保持任务有效性。现有方法通常结合时间与符号监督从人类演示中推断任务结构，但符号谓词需要明确的接地，这在现实场景中难以获取。本研究提出一种仅使用简单运动学图和相对物体位姿分布，从演示中提取任务依赖结构的方法：从这些表示中，我们的方法估计成对任务步骤依赖概率，并将其用于初始化优先关系图的边权重；随后引入过滤流程，将该概率估计图转换为最终任务依赖图。我们在现有基准和包含更长、依赖关系更复杂的新数据集上评估该方法，发现与基线相比，我们的方法能从更少演示中恢复更准确的任务结构；最后，我们证明推断出的图可用于为同一任务生成多个有效的机器人执行顺序。
英文摘要
Long-horizon manipulation tasks are often only partially ordered. For example, when assembling an electronic device, the battery and circuit board may be installed in either order, but both must be in place before the enclosure is closed. Recovering such dependencies enables robots to flexibly reorder subtasks while preserving task validity. Existing approaches typically infer task structure from human demonstrations using both temporal and symbolic supervision. However, symbolic predicates require explicit grounding, which is difficult to obtain in realistic settings. In this work, we present an approach for extracting task dependency structures from demonstrations using only simple kinematic graphs and distributions over relative object poses. From these representations, our method estimates pairwise task-step-dependency probabilities and uses them to initialize the edge weights of a precedence graph. We then introduce a filtering pipeline that converts this graph of probability estimates into the final task dependency graph. We evaluate our approach on an existing benchmark and on a new dataset comprising longer tasks with more complex dependencies. We find that our method recovers more accurate task structures from fewer demonstrations than the baselines. Finally, we demonstrate that the inferred graphs can be used to generate multiple valid robotic execution orders for the same task.
发表机构
- University of Freiburg（弗莱堡大学）
- Czech Technical University（捷克技术大学）
- Czech Institute of Informatics, Robotics, and Cybernetics（捷克信息学、机器人学与控制论研究所）
机构由 AI 辅助整理，请以论文原文为准。
