Enabling Robots to Understand Incomplete Natural Language Instructions Using Commonsense Reasoning
arXiv:1904.12907
Abstract
Enabling robots to understand instructions provided via spoken natural language would facilitate interaction between robots and people in a variety of settings in homes and workplaces. However, natural language instructions are often missing information that would be obvious to a human based on environmental context and common sense, and hence does not need to be explicitly stated. In this paper, we introduce Language-Model-based Commonsense Reasoning (LMCR), a new method which enables a robot to listen to a natural language instruction from a human, observe the environment around it, and automatically fill in information missing from the instruction using environmental context and a new commonsense reasoning approach. Our approach first converts an instruction provided as unconstrained natural language into a form that a robot can understand by parsing it into verb frames. Our approach then fills in missing information in the instruction by observing objects in its vicinity and leveraging commonsense reasoning. To learn commonsense reasoning automatically, our approach distills knowledge from large unstructured textual corpora by training a language model. Our results show the feasibility of a robot learning commonsense knowledge automatically from web-based textual corpora, and the power of learned commonsense reasoning models in enabling a robot to autonomously perform tasks based on incomplete natural language instructions.
arXiv comment7 pages, 4 figures, ICRA 2020
References in corpus (9)
- ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
- Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars
- A Simple Method for Commonsense Reasoning
- Towards Automatic Learning of Procedures from Web Instructional Videos
- RoboBrain: Large-Scale Knowledge Engine for Robots
- Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction
- FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning
- Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation
- Attention Based Natural Language Grounding by Navigating Virtual Environment