Abstract
Natural human-like human-robot interaction (NHL-HRI) requires the robot to be skilled both at recognizing and producing many subtle human behaviors, often taken for granted by humans. We suggest a rough division of these requirements for NHL-HRI into three classes of properties: (1) social behaviors, (2) goal-oriented cognition, and (3) robust intelligence, and present the novel DIARC architecture for complex affective robots for human-robot interaction, which aims to meet some of those requirements. We briefly describe the functional properties of DIARC and its implementation in our ADE system. Then we report results from human subject evaluations in the laboratory as well as our experiences with the robot running ADE at the 2005 AAAI Robot Competition in the Open Interaction Event and Robot Exhibition.
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Note that the emphasis here is on both “consistency” and “extended time period” as humans can sometimes be tricked into believing that something has a purpose because it seems to exhibit purposeful behavior for short periods of time. However, the deception will typically not last for long (e.g., see the repeatedly failed attempts at convincing humans that a computer is a human in the Loebner prize competition).
The ASCII representation is generated with the binary program distributed on Lowe's website http://www.cs.ubc.ca/∼lowe/keypoints/.
The current implementation of the action interpreter is still somewhat impoverished, as variables for other scripts have not been implemented yet. For example, it is not possible to add “variable actions” to scripts such as “pick any script that satisfies preconditions \(X_i\) and execute it”, which would cause the action interpreter to search through its scripts and match them against the preconditions \(X_i\). Also, the current implementation only supports detection of failures, but not “recursive” attempts to recover from them—“recursive”, for recovery actions might themselves fail and might thus lead to recovery from recovery, etc.
Automatic failure recovery procedures have since been added to the infrastructure to address this problem; see also Section 4.
This problem has since been addressed by applying a stereo vision algorithm to isolate an object in the foreground. Preprocessing the stereo image increases the likelihood that any of the determined keypoints actually belong to the object held to the camera.
The details for reprioritization of goals were not provided in Breazeal et al. (2004).
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Acknowledgment
The authors would like to thank Virgil Andronache, Chris Middendorff, Aaron Dingler, Peter Bui and Patrick Davis for their help with the implementation.
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Scheutz, M., Schermerhorn, P., Kramer, J. et al. First steps toward natural human-like HRI. Auton Robot 22, 411–423 (2007). https://doi.org/10.1007/s10514-006-9018-3
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DOI: https://doi.org/10.1007/s10514-006-9018-3