Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction
Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction
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DOI：
10.1109/biorob52689.2022.9925337
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
George L. Holmes;Keyri Moreno Bonnett;Amy Costa;Devin M. Burns;Yun Seong Song
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文献类型：
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作者：
George L. Holmes;Keyri Moreno Bonnett;Amy Costa;Devin M. Burns;Yun Seong Song
这项工作挑战了物理人机交互（pHRI）中的常见假设，即人类用户的运动意图可以简单地用与力和运动相关的动态方程来建模，而不管用户是谁。在物理人与人的互动（pHHI）的研究表明，互动力携带复杂的信息，揭示运动技能和角色的伙伴关系，甚至促进适应和运动学习。在这种观点下，pHRI研究中经常使用的简单力-位移方程可能是不够的。为了验证这一点，这项工作测量和分析了两个人之间的相互作用力（F），领导者引导被蒙上眼睛的跟随者走一条随机选择的路径。跟随者的实际轨迹被转换为速度命令（$V$），这将允许一个假设的机器人跟随者跟踪相同的轨迹。然后，$F$和$V$之间的可能的分析关系，得到使用神经网络训练。结果表明，虽然$F$有助于预测V，但这种关系并不简单，$F$的看似不相关的组件可能很重要，力-速度关系对每个人类追随者来说都是独一无二的，人类神经对运动的控制可能会影响运动意图的预测。有人建议，用户特定的，无刻板印象的控制器可以更准确地解码人类意图的pHRI。
This work challenges the common assumption in physical human-robot interaction (pHRI) that the movement intention of a human user can be simply modeled with dynamic equations relating forces to movements, regardless of the user. Studies in physical human-human interaction (pHHI) suggest that interaction forces carry sophisticated information that reveals motor skills and roles in the partnership and even promotes adaptation and motor learning. In this view, simple force-displacement equations often used in pHRI studies may not be sufficient. To test this, this work measured and analyzed the interaction forces (F) between two humans as the leader guided the blindfolded follower on a randomly chosen path. The actual trajectory of the follower was transformed to the velocity commands ($V$) that would allow a hypothetical robot follower to track the same trajectory. Then, possible analytical relationships between $F$ and $V$ were obtained using neural network training. Results suggest that while $F$ helps predict V, the relationship is not straightforward, that seemingly irrelevant components of $F$ may be important, that force-velocity relationships are unique to each human follower, and that human neural control of movement may affect the prediction of the movement intent. It is suggested that user-specific, stereotype-free controllers may more accurately decode human intent in pHRI.