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We therefore demonstrate the task of learning to summarize and answer questions about a robot agent\u2019s past actions using natural language alone. A single system with a large language model at its core is trained to both summarize and answer questions about action sequences given ego-centric video frames of a virtual robot and a question prompt. To enable training of question answering, we develop a method to automatically generate English-language questions and answers about objects, actions, and the temporal order in which actions occurred during episodes of robot action in the virtual environment. Training one model to both summarize and answer questions enables zero-shot transfer of representations of objects learned through question answering to improved action summarization. \n<\/jats:p>","DOI":"10.1007\/s10514-023-10134-4","type":"journal-article","created":{"date-parts":[[2023,11,16]],"date-time":"2023-11-16T18:01:51Z","timestamp":1700157711000},"page":"1103-1118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Learning to summarize and answer questions about a virtual robot\u2019s past actions"],"prefix":"10.1007","volume":"47","author":[{"given":"Chad","family":"DeChant","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iretiayo","family":"Akinola","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Bauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,16]]},"reference":[{"key":"10134_CR1","doi-asserted-by":"crossref","unstructured":"Anderson, P., Wu, Q., Teney, D., et\u00a0al. 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