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

TL;DR

This paper introduces a novel method called Dynamic Constraint Mapping that translates attribute-based natural language instructions into optimized robot motion plans, enabling robots to understand and execute complex commands in dynamic environments.

Contribution

The paper proposes a new algorithm that combines NLP and motion planning using Dynamic Constraint Mapping and a Dynamic Grounding Graph to generate smooth robot trajectories from natural language instructions.

Findings

Effective in simulated environments with complex language commands

Handles negation, orientation, and distance constraints

Produces smooth trajectories in dynamic scenes

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.