CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Interactive robot task training through dialog and demonstration
Proceedings of the ACM/IEEE international conference on Human-robot interaction
Object schemas for grounding language in a responsive robot
Connection Science - Language and Robots
Walk the talk: connecting language, knowledge, and action in route instructions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Interactive policy learning through confidence-based autonomy
Journal of Artificial Intelligence Research
Reinforcement learning for mapping instructions to actions
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Toward understanding natural language directions
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Learning to follow navigational directions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning dependency-based compositional semantics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Spatial language for human-robot dialogs
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In order for robots to effectively understand natural language commands, they must be able to acquire a large vocabulary of meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach which is capable of jointly learning a policy for following natural language commands such as "Pick up the tire pallet," as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words "the tire pallet" and a specific object in the environment. We assume the action policy takes a parametric form that factors based on the structure of the language, based on the G3 framework and use stochastic gradient ascent to optimize policy parameters. Our preliminary evaluation demonstrates the effectiveness of the model on a corpus of "pick up" commands given to a robotic forklift by untrained users.