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Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically -- continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.
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Robots That Use Language
This article surveys the use of natural language in robotics from a robotics point of view. To use human language, robots must map words to aspects of the physical world, mediated by the robot's sensors and actuators. This problem differs from other natural language processing domains due to the need to ground the language to noisy percepts and physical actions. Here, we describe central aspects of language use by robots, including understanding natural language requests, using language to drive learning about the physical world, and engaging in collaborative dialogue with a human partner. We describe common approaches, roughly divided into learning methods, logic-based methods, and methods that focus on questions of human–robot interaction. Finally, we describe several application domains for language-using robots.
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- Award ID(s):
- 1637614
- PAR ID:
- 10162292
- Date Published:
- Journal Name:
- Annual Review of Control, Robotics, and Autonomous Systems
- Volume:
- 3
- Issue:
- 1
- ISSN:
- 2573-5144
- Page Range / eLocation ID:
- 25 to 55
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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For robots deployed in human-centric spaces, natural language promises an intuitive, natural interface. However, obtaining appropriate training data for grounded language in a variety of settings is a significant barrier. In this work, we describe using human-robot interactions in virtual reality to train a robot, combining fully simulated sensing and actuation with human interaction. We present the architecture of our simulator and our grounded language learning approach, then describe our intended initial experiments.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1146/annurev-control-101119-071628
