Deep compositional robotic planners that follow natural language commands
Yen-Ling Kuo,Boris Katz,Andrei Barbu,Yen-Ling Kuo,Boris Katz,Andrei Barbu
We demonstrate how a sampling-based robotic planner can be augmented to learn to understand a sequence of natural language commands in a continuous configuration space to move and manipulate objects. Our approach combines a deep network structured according to the parse of a complex command that includes objects, verbs, spatial relations, and attributes, with a sampling-based planner, RRT. A recur...
