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Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2021-07-02 , DOI: 10.1109/lra.2021.3094479 Loris Roveda , Beatrice Maggioni , Elia Marescotti , Asad Ali Shahid , Andrea Maria Zanchettin , Alberto Bemporad , Dario Piga
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2021-07-02 , DOI: 10.1109/lra.2021.3094479 Loris Roveda , Beatrice Maggioni , Elia Marescotti , Asad Ali Shahid , Andrea Maria Zanchettin , Alberto Bemporad , Dario Piga
Production plants are being re-designed to implement human-centered solutions. Especially considering high added-value operations, robots are required to optimize their behavior to achieve a task quality at least comparable to the one obtained by the skilled operators. A manual programming and tuning of the manipulator is not an efficient solution, requiring to adopt towards automated strategies. Adding external sensors (
e.g.
, cameras) increases the robotic cell complexity and it doesn’t solve the issue since it is usually difficult to build explicit reward functions measuring the robot performance, while it is easier for the user to define a qualitative comparison between two experiments. According to these needs, in this letter, the recently-developed preferences-based optimization approach GLISp is employed and adapted to tune the novel developed path-based velocity planner. The implemented solution defines an intuitive human-centered procedure, capable of transferring (through pairwise preferences between experiments) the task knowledge from the operator to the manipulator. A Franka EMIKA panda robot has been employed as a test platform to perform a robotic sealing task (
i.e.
, material deposition task), validating the proposed methodology. The proposed approach has been compared with a programming by demonstration approach, and with the manual tuning of the path-based velocity planner. Achieved results demonstrate the improved deposition quality obtained with the proposed optimized path-based velocity planner methodology in a limited number of experimental trials (20).
中文翻译:
机器人密封任务中基于路径的速度规划器的基于成对偏好的优化
生产工厂正在重新设计,以实施以人为本的解决方案。特别是考虑到高附加值的操作,机器人需要优化其行为,以达到至少与熟练操作员所获得的任务质量相当的任务质量。机械手的手动编程和调整不是一种有效的解决方案,需要采用自动化策略。添加外部传感器(例如,相机)增加了机器人单元的复杂性,但它并没有解决问题,因为通常很难构建测量机器人性能的显式奖励函数,而用户更容易定义两个实验之间的定性比较。根据这些需求,在这封信中,最近开发的基于偏好的优化方法 GLISp 被采用并适用于调整新开发的基于路径的速度规划器。实施的解决方案定义了一个直观的以人为中心的程序,能够(通过实验之间的成对偏好)将任务知识从操作员转移到机械手。Franka EMIKA 熊猫机器人已被用作执行机器人密封任务的测试平台(
即,材料沉积任务),验证所提出的方法。已将所提出的方法与演示方法的编程以及基于路径的速度规划器的手动调整进行了比较。获得的结果表明,在有限数量的实验试验中,使用所提出的优化的基于路径的速度规划器方法获得了改进的沉积质量 (20)。
更新日期:2021-07-23
中文翻译:
机器人密封任务中基于路径的速度规划器的基于成对偏好的优化
生产工厂正在重新设计,以实施以人为本的解决方案。特别是考虑到高附加值的操作,机器人需要优化其行为,以达到至少与熟练操作员所获得的任务质量相当的任务质量。机械手的手动编程和调整不是一种有效的解决方案,需要采用自动化策略。添加外部传感器(