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Additionally, such robots should be able to explain their decisions after the fact, to improve accountability and confidence in their behavior. To address this, we present a two-layered proactive system that extracts high-level social features from low-level perceptions and uses these features to make high-level decisions regarding the initiation and maintenance of human-robot interactions. With this system outlined, the primary focus of this work is then a novel method to generate counterfactual explanations in response to a variety of contrastive queries. We provide an early proof of concept to illustrate how these explanations can be generated by leveraging the two-layer system.","This work was supported by Horizon Europe under the MSCA grant agreement No 101072488 (TRAIL); by the \"European Union NextGenerationEU/PRTR\" project CHLOE-GRAPH PID2020-118649RBI00 funded by MCIN/ AEI /10.13039/ 501100011033; A. 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