Human-robot interactions are less efficient and communicative than human-to-human interactions, and a key reason is a lack of informed sense of touch in robotic systems. Existing literature demonstrates robot success in executing handovers with humans, albeit with substantial reliance on external sensing or with primitive signal processing methods, deficient compared to the rich set of information humans can detect. In contrast, we present models capable of distinguishing between four classes of human tactile gestures at a robot's end effector, using only a non-collocated six-axis force sensor at the wrist. Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction . We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural network classifiers on the raw data outperform several other combinations of algorithms and feature sets .
Physical human-robot interactions (pHRI) are less efficient and communicative than human-human interactions, and a key reason is a lack of informative sense of touch in robotic systems. Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover the majority of the gesture characteristics identified in the literature, 2 ) the collection of an extensive force dataset on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset . We demonstrate high classification accuracies among our proposed gesture definitions on a test set, emphasizing that neural network classifiers on the raw data outperform other combinations of feature sets and algorithms .
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Human-robot interactions
Physical human-robot interactions (pHRI)
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human-to-human
human-human
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informed
informative
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Existing literature demonstrates robot success in executing handovers with humans, albeit with substantial reliance on external sensing or with primitive signal processing methods, deficient compared to the rich set of information humans can detect. In contrast, we present models capable of distinguishing between four classes of human tactile gestures at a robot's end effector, using only a non-collocated six-axis force sensor at the wrist. Due to the absence
Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover the majority of the gesture characteristics identified
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this work describes 1
2
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characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction
on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset
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several
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algorithms and feature sets
feature sets and algorithms