From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots
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arXiv
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| Format: | Preprint |
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2019
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| _version_ | 1866910340978049024 |
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| author | Zhang, Yu |
| author_facet | Zhang, Yu |
| contents | In this paper, we consider a first step to bridge a gap in coordinating task planning robots. Specifically, we study the automatic construction of languages that are maximally flexible while being sufficiently explicative for coordination. To this end, we view language as a machinery for specifying temporal-state constraints of plans. Such a view enables us to reverse-engineer a language from the ground up by mapping these composable constraints to words. Our language expresses a plan for any given task as a "plan sketch" to convey just-enough details while maximizing the flexibility to realize it, leading to robust coordination with optimality guarantees among other benefits. We formulate and analyze the problem, provide an approximate solution, and validate the advantages of our approach under various scenarios to shed light on its applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1905_00517 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots Zhang, Yu Artificial Intelligence In this paper, we consider a first step to bridge a gap in coordinating task planning robots. Specifically, we study the automatic construction of languages that are maximally flexible while being sufficiently explicative for coordination. To this end, we view language as a machinery for specifying temporal-state constraints of plans. Such a view enables us to reverse-engineer a language from the ground up by mapping these composable constraints to words. Our language expresses a plan for any given task as a "plan sketch" to convey just-enough details while maximizing the flexibility to realize it, leading to robust coordination with optimality guarantees among other benefits. We formulate and analyze the problem, provide an approximate solution, and validate the advantages of our approach under various scenarios to shed light on its applications. |
| title | From Abstractions to Grounded Languages for Robust Coordination of Task Planning Robots |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/1905.00517 |