A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions

Siddharth Karamcheti, Edward Clem Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L.S. Wong, Stefanie Tellex
2017 Proceedings of the First Workshop on Language Grounding for Robotics  
Robots operating alongside humans in diverse, stochastic environments must be able to accurately interpret natural language commands. These instructions often fall into one of two categories: those that specify a goal condition or target state, and those that specify explicit actions, or how to perform a given task. Recent approaches have used reward functions as a semantic representation of goal-based commands, which allows for the use of a state-of-the-art planner to find a policy for the
more » ... n task. However, these reward functions cannot be directly used to represent action-oriented commands. We introduce a new hybrid approach, the Deep Recurrent Action-Goal Grounding Network (DRAGGN), for task grounding and execution that handles natural language from either category as input, and generalizes to unseen environments. Our robotsimulation results demonstrate that a system successfully interpreting both goaloriented and action-oriented task specifications brings us closer to robust natural language understanding for human-robot interaction.
doi:10.18653/v1/w17-2809 dblp:conf/acl/KaramchetiWARGW17 fatcat:aqv5k2cd6nazxbce6r5ig46wdq