Abstract
We present an approach to enable an autonomous agent (learner) in building a model of a new unknown robot’s (subject) performance at a task through experimentation. The subject’s appearance can provide cues to its physical as well as cognitive capabilities. Building on these cues, our active experimentation approach learns a model that captures the effect of relevant extrinsic factors on the subject’s ability to perform a task. As personal robots become increasingly multi-functional and adaptive, such autonomous agents would find use as tools for humans in determining “What can this robot do?”. We applied our algorithm in modelling a NAO and a Pepper robot at two different tasks. We first demonstrate the advantages of our active experimentation approach, then we show the utility of such models in identifying scenarios a robot is well suited for, in performing a task.
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Notes
- 1.
An instantiation of a set of random variables is a mapping from variables in the set to values in their domain. Query \(\leftarrow \cup _{q\in \text {Q}}\{q:v_{q}\}\) where \(v_{q}\in {Domain(q)}\).
- 2.
, \(P_1\) and \(P_2\) are distributions of o.
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Acknowledgements
This research is partially sponsored by DARPA under agreements FA87501620042 and FA87501720152 and NSF under grant IIS1637927. The views and conclusions contained in this document are those of the authors only.
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Khadke, A., Veloso, M. (2019). Inferring Capabilities by Experimentation. In: Strand, M., Dillmann, R., Menegatti, E., Ghidoni, S. (eds) Intelligent Autonomous Systems 15. IAS 2018. Advances in Intelligent Systems and Computing, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-01370-7_69
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