Purpose

Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment.

Design/methodology/approach

The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment.

Findings

The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment.

Originality/value

This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

中文翻译：

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基于动态运动原语的云机器人技能学习，可避免点和非点障碍

目的

动态运动原语（DMP）是从演示方法中学到的一般机器人技能，但通常用于单个机器人操作。对于基于云的机器人技能学习，作者考虑了环境所改变的轨迹/技能，重建了DMPs模型，并提出了一个新的基于DMPs的技能学习框架，该框架消除了不断变化的环境的影响。

设计/方法/方法

作者提出了两种避障场景的方法：点障碍和非点障碍。对于具有点障碍的情况，将加速项添加到原始DMP功能。此术语中的未知参数是通过强制功能的交互式标识和拟合步骤估算的。然后，获得了轻视障碍物影响的纯技巧。使用已识别的参数，该技能可以应用于有障碍的新任务。对于非点障碍物的情况，提出了一种从无障碍物的通用空间到障碍物凝聚空间的匹配函数的空间匹配方法。然后，原始轨迹将随着空间的变换而改变，从而获得新环境的一般轨迹。

发现

所提出的两种方法均通过两项实验验证，其中一项实验是基于Omni游戏杆来记录操作员的操纵运动。结果表明，所学技能使机器人能够在新环境中执行诸如自动组装之类的任务。

创意/价值

这是一项新的创新，用于从多场景任务中学习基于DMP的云机器人技能，并随着环境的变化而推广新技能。