Language-Conditioned Imitation Learning for Robot Manipulation Tasks

TL;DR

This paper introduces a method for integrating natural language descriptions into imitation learning to enable robots to understand and execute manipulation tasks based on verbal instructions, improving control and reducing ambiguity.

Contribution

The paper presents a novel approach that combines language, perception, and motion data during training to create language-conditioned policies for robot manipulation.

Findings

Effective learning of language-conditioned policies in simulation

Improved control and interpretability of robot actions with natural language cues

Comparison shows advantages over alternative methods

Abstract

Imitation learning is a popular approach for teaching motor skills to robots. However, most approaches focus on extracting policy parameters from execution traces alone (i.e., motion trajectories and perceptual data). No adequate communication channel exists between the human expert and the robot to describe critical aspects of the task, such as the properties of the target object or the intended shape of the motion. Motivated by insights into the human teaching process, we introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent (e.g., "go to the large green bowl"). The training process then interrelates these two modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at runtime on new human commands and instructions, which allows for more fine-grained control over the trained policies while also reducing situational ambiguity. We demonstrate in a set of simulation experiments how our approach can learn language-conditioned manipulation policies for a seven-degree-of-freedom robot arm and compare the results to a variety of alternative methods.