Abstract One of the problems that service robotics deals with is to bring mobile manipulators to work in semi-structured human scenarios, which requires an efficient and flexible way to execute every-day tasks, like serve a cup in a cluttered environment. Usually, for those tasks, the combination of symbolic and geometric levels of planning is necessary, as well as the integration of perception models with knowledge to guide both planning levels, resulting in a sequence of actions or skills which, according to the current knowledge of the world, may be executed. This paper proposes a planning and execution framework, called SkillMaN, for robotic manipulation tasks, which is equipped with a module with experiential knowledge (learned from its experience or given by the user) on how to execute a set of skills, like pick-up, put-down or open a drawer, using workflows as well as robot trajectories. The framework also contains an execution assistant with geometric tools and reasoning capabilities to manage how to actually execute the sequence of motions to perform a manipulation task (which are forwarded to the executor module), as well as the capacity to store the relevant information to the experiential knowledge for further usage, and the capacity to interpret the actual perceived situation (in case the preconditions of an action do not hold) and to feed back the updated state to the planner to resume from there, allowing the robot to adapt to non-expected situations. To evaluate the viability of the proposed framework, an experiment has been proposed involving different skills performed with various types of objects in different scene contexts.

中文翻译：

---

SkillMaN - 基于感知和推理的基于技能的机器人操作框架

摘要 服务机器人解决的问题之一是将移动机械手带到半结构化的人类场景中工作，这需要一种高效灵活的方式来执行日常任务，例如在杂乱的环境中提供杯子。通常，对于这些任务，需要结合规划的符号和几何层次，以及将感知模型与知识相结合来指导两个规划层次，从而产生一系列动作或技能，根据当前的知识世界，可能会被处决。本文提出了一个名为 SkillMaN 的机器人操作任务的规划和执行框架，该框架配备了一个模块，该模块具有关于如何执行一组技能的经验知识（从其经验中学习或由用户提供），例如拾取，放下或打开抽屉，使用工作流程以及机器人轨迹。该框架还包含一个具有几何工具和推理能力的执行助手，用于管理如何实际执行运动序列以执行操作任务（转发到执行器模块），以及将相关信息存储到进一步使用的经验知识，以及解释实际感知情况的能力（如果行动的先决条件不成立）并将更新的状态反馈给规划者以从那里恢复，允许机器人适应非预期的情况。为了评估所提出框架的可行性，已经提出了一项实验，该实验涉及在不同场景上下文中对各种类型的对象执行的不同技能。该框架还包含一个具有几何工具和推理能力的执行助手，用于管理如何实际执行运动序列以执行操作任务（转发到执行器模块），以及将相关信息存储到进一步使用的经验知识，以及解释实际感知情况的能力（如果行动的先决条件不成立）并将更新的状态反馈给规划者以从那里恢复，允许机器人适应非预期的情况。为了评估所提出框架的可行性，已经提出了一项实验，该实验涉及在不同场景上下文中对各种类型的对象执行的不同技能。该框架还包含一个具有几何工具和推理能力的执行助手，用于管理如何实际执行运动序列以执行操作任务（转发到执行器模块），以及将相关信息存储到进一步使用的经验知识，以及解释实际感知情况的能力（如果行动的先决条件不成立）并将更新的状态反馈给规划者以从那里恢复，允许机器人适应非预期的情况。为了评估所提出框架的可行性，已经提出了一项实验，该实验涉及在不同场景上下文中对各种类型的对象执行的不同技能。