# Prospection: Interpretable Plans From Language By Predicting the Future

## TL;DR

This paper introduces a framework that enables robots to interpret natural language commands by generating interpretable plans with intermediate goals, using prospection to predict future consequences of actions for improved reasoning.

## Contribution

It presents a novel approach that combines language understanding with predictive planning, allowing robots to generate interpretable, goal-oriented plans from natural language instructions.

## Key findings

- Plans generated align well with human commands in simulated environments
- The framework effectively predicts future consequences of actions
- Demonstrates interpretability and fidelity of plans from natural language

## Abstract

High-level human instructions often correspond to behaviors with multiple implicit steps. In order for robots to be useful in the real world, they must be able to to reason over both motions and intermediate goals implied by human instructions. In this work, we propose a framework for learning representations that convert from a natural-language command to a sequence of intermediate goals for execution on a robot. A key feature of this framework is prospection, training an agent not just to correctly execute the prescribed command, but to predict a horizon of consequences of an action before taking it. We demonstrate the fidelity of plans generated by our framework when interpreting real, crowd-sourced natural language commands for a robot in simulated scenes.