Inferring Maps and Behaviors from Natural Language Instructions
| Autor: | Matthew R. Walter, Jean Oh, Seth Teller, Sachithra Hemachandra, Nicholas Roy, Felix Duvallet, Thomas M. Howard, Anthony Stentz |
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| Rok vydání: | 2015 |
| Předmět: |
0209 industrial biotechnology
Computer science Parse tree Natural language understanding Novelty Mobile robot 02 engineering and technology computer.software_genre 020901 industrial engineering & automation Human–computer interaction Metric (mathematics) 0202 electrical engineering electronic engineering information engineering Robot A priori and a posteriori 020201 artificial intelligence & image processing computer Natural language |
| Zdroj: | Experimental Robotics ISBN: 9783319237770 ISER |
| Popis: | Natural language provides a flexible, intuitive way for people to command robots, which is becoming increasingly important as robots transition to working alongside people in our homes and workplaces. To follow instructions in unknown environments, robots will be expected to reason about parts of the environments that were described in the instruction, but that the robot has no direct knowledge about. However, most existing approaches to natural language understanding require that the robot’s environment be known a priori. This paper proposes a probabilistic framework that enables robots to follow commands given in natural language, without any prior knowledge of the environment. The novelty lies in exploiting environment information implicit in the instruction, thereby treating language as a type of sensor that is used to formulate a prior distribution over the unknown parts of the environment. The algorithm then uses this learned distribution to infer a sequence of actions that are most consistent with the command, updating our belief as we gather more metric information. We evaluate our approach through simulation as well as experiments on two mobile robots; our results demonstrate the algorithm’s ability to follow navigation commands with performance comparable to that of a fully-known environment. |
| Databáze: | OpenAIRE |
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