Learning Intention Aware Online Adaptation of Movement Primitives
Learning Intention Aware Online Adaptation of Movement Primitives
复制标题
学习运动原语的意图感知在线适应
DOI：
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发表时间：
2019
影响因子：
5.2
通讯作者：
Jan Peters
中科院分区：
文献类型：
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作者：
Dorothea Koert;J. Pajarinen;Albert Schotschneider;Susanne Trick;C. Rothkopf;Jan Peters
为了在接近非专家的情况下进行操作，未来的机器人既需要外行人可以理解的直观指令形式，又需要能够对人类同事做出适当的反应。使用概率运动原语 (ProMP) 进行模仿学习的指令允许通过从演示中学习机器人轨迹（包括运动可变性）来捕获任务。然而，在执行所学动作期间对人类同事的适当反应对于流畅的任务执行、感知安全性和主观舒适度至关重要。为了在人机交互中促进这种适当的响应行为，机器人需要能够在 ProMP 执行期间在线对其人类工作空间同伴做出反应。因此，我们从人类动作中学习基于目标的意图预测模型。使用这种概率模型，我们向 ProMP 引入了意图感知在线适应。我们比较了两种不同的新颖方法：首先，在线空间变形，它通过在执行过程中动态改变 ProMP 轨迹的形状来避免碰撞，同时保持接近演示的运动；其次，在线时间缩放，它调整 ProMP 的速度曲线以避免时间相关的碰撞。我们在非专家用户的实验中评估了这两种方法。与机器人的非自适应行为相比，受试者报告了更高水平的感知安全性，并且在意图感知适应期间（特别是在空间变形期间）感到更少的干扰。
In order to operate close to non-experts, future robots require both an intuitive form of instruction accessible to laymen and the ability to react appropriately to a human co-worker. Instruction by imitation learning with probabilistic movement primitives (ProMPs) allows capturing tasks by learning robot trajectories from demonstrations, including the motion variability. However, appropriate responses to human co-workers during the execution of the learned movements are crucial for fluent task execution, perceived safety, and subjective comfort. To facilitate such appropriate responsive behaviors in human–robot interaction, the robot needs to be able to react to its human workspace co-inhabitant online during the execution of the ProMPs. Thus, we learn a goal-based intention prediction model from human motions. Using this probabilistic model, we introduce intention-aware online adaptation to ProMPs. We compare two different novel approaches: First, online spatial deformation, which avoids collisions by changing the shape of the ProMP trajectories dynamically during execution while staying close to the demonstrated motions and second, online temporal scaling, which adapts the velocity profile of a ProMP to avoid time-dependent collisions. We evaluate both approaches in experiments with non-expert users. The subjects reported a higher level of perceived safety and felt less disturbed during intention aware adaptation, in particular during spatial deformation, compared to non-adaptive behavior of the robot.