DC Field | Value | Language |
---|---|---|
dc.contributor.author | Nyga, Daniel | ko |
dc.contributor.author | Roy, Subhro | ko |
dc.contributor.author | Paul, Rohan | ko |
dc.contributor.author | Park, Daehyung | ko |
dc.contributor.author | Pomarlan, Mihai | ko |
dc.contributor.author | Beetz, Michael | ko |
dc.contributor.author | Roy, Nicholas | ko |
dc.date.accessioned | 2020-11-24T07:10:13Z | - |
dc.date.available | 2020-11-24T07:10:13Z | - |
dc.date.created | 2020-11-20 | - |
dc.date.created | 2020-11-20 | - |
dc.date.issued | 2018-10 | - |
dc.identifier.citation | 2018 Conference on Robot Learning (CoRL 2018) | - |
dc.identifier.uri | http://hdl.handle.net/10203/277548 | - |
dc.description.abstract | Our goal is to enable robots to interpret and execute high-level tasks conveyed using natural language instructions. For example, consider tasking a household robot to, “prepare my breakfast”, “clear the boxes on the table” or “make me a fruit milkshake”. Interpreting such underspecified instructions requires environmental context and background knowledge about how to accomplish complex tasks. Further, the robot’s workspace knowledge may be incomplete: the environment may only be partially-observed or background knowledge may be missing causing a failure in plan synthesis. We introduce a probabilistic model that utilizes background knowledge to infer latent or missing plan constituents based on semantic co-associations learned from noisy textual corpora of task descriptions. The ability to infer missing plan constituents enables information-seeking actions such as visual exploration or dialogue with the human to acquire new knowledge to fill incomplete plans. Results indicate robust plan inference from under-specified instructions in partially-known worlds. | - |
dc.language | English | - |
dc.publisher | PMLR | - |
dc.title | Grounding Robot Plans from Natural Language Instructions with Incomplete World Knowledge | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 2018 Conference on Robot Learning (CoRL 2018) | - |
dc.identifier.conferencecountry | SZ | - |
dc.identifier.conferencelocation | Zurich | - |
dc.contributor.localauthor | Park, Daehyung | - |
dc.contributor.nonIdAuthor | Nyga, Daniel | - |
dc.contributor.nonIdAuthor | Roy, Subhro | - |
dc.contributor.nonIdAuthor | Paul, Rohan | - |
dc.contributor.nonIdAuthor | Pomarlan, Mihai | - |
dc.contributor.nonIdAuthor | Beetz, Michael | - |
dc.contributor.nonIdAuthor | Roy, Nicholas | - |
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