Efficient Generation of Motion Plans from Attribute-Based Natural Language Instructions Using Dynamic Constraint Mapping

Abstract

We present an algorithm for combining natural language processing (NLP) and fast robot motion planning to automatically generate robot movements. Our formulation uses a novel concept called Dynamic Constraint Mapping to transform complex, attribute-based natural language instructions into appropriate cost functions and parametric constraints for optimization-based motion planning. We generate a factor graph from natural language instructions called the Dynamic Grounding Graph (DGG), which takes latent parameters into account. The coefficients of this factor graph are learned based on conditional random fields (CRFs) and are used to dynamically generate the constraints for motion planning. We map the cost function directly to the motion parameters of the planner and compute smooth trajectories in dynamic scenes. We highlight the performance of our approach in a simulated environment and via a human interacting with a 7-DOF Fetch robot using intricate language commands including negation, orientation specification, and distance constraints.

MLNet: a multi-level multimodal named entity recognition architecture

“…In the field of human–computer interaction, a large number of novel techniques have been proposed to improve efficiency, reduce operational difficulty, and increase recognition accuracy. Currently, natural language-based human–robot interaction has been widely used (Ahn et al, 2018 ; Park et al, 2019 ; Walker et al, 2019 ). At the same time, the field of image perception is also developing rapidly.…”